{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X62lLTypujDU",
    "tags": []
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "lU5OiA0FudxK"
   },
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Library to suppress warnings or deprecation notes\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-bcb2270c-a725-11ed-9d3c-9cebe82f4a87</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58600/status\" target=\"_blank\">http://127.0.0.1:58600/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">8a965852</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:58600/status\" target=\"_blank\">http://127.0.0.1:58600/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 4\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 4\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 31.90 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-a08f4d1e-a8fb-49e8-aa6a-27cc18358d6d</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:58601\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:58600/status\" target=\"_blank\">http://127.0.0.1:58600/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 4\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 31.90 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:58621\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58624/status\" target=\"_blank\">http://127.0.0.1:58624/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.98 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:58604\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\tedesco\\AppData\\Local\\Temp\\dask-worker-space\\worker-wdqhw5ku\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:58620\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58622/status\" target=\"_blank\">http://127.0.0.1:58622/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.98 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:58605\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\tedesco\\AppData\\Local\\Temp\\dask-worker-space\\worker-mom2bk04\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:58629\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58630/status\" target=\"_blank\">http://127.0.0.1:58630/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.98 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:58606\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\tedesco\\AppData\\Local\\Temp\\dask-worker-space\\worker-w1q8bgjt\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:58626\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58627/status\" target=\"_blank\">http://127.0.0.1:58627/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 7.98 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:58607\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\tedesco\\AppData\\Local\\Temp\\dask-worker-space\\worker-zgha5msu\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:58601' processes=4 threads=4, memory=31.90 GiB>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from dask.distributed import Client\n",
    "\n",
    "client = Client(n_workers=4, threads_per_worker=1)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#METRIC CLASSFICATION REPORT\n",
    "def classification_report_to_dataframe(str_representation_of_report):\n",
    "    split_string = [x.split(' ') for x in str_representation_of_report.split('\\n')]\n",
    "    column_names = ['']+[x for x in split_string[0] if x!='']\n",
    "    values = []\n",
    "    for table_row in split_string[1:-1]:\n",
    "        table_row = [value for value in table_row if value!='']\n",
    "        if table_row!=[]:\n",
    "            values.append(table_row)\n",
    "    for i in values:\n",
    "        for j in range(len(i)):\n",
    "            if i[1] == 'avg':\n",
    "                i[0:2] = [' '.join(i[0:2])]\n",
    "            if len(i) == 3:\n",
    "                i.insert(1,np.nan)\n",
    "                i.insert(2, np.nan)\n",
    "            else:\n",
    "                pass\n",
    "    report_to_df = pd.DataFrame(data=values, columns=column_names)\n",
    "    return report_to_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining a function to compute different metrics to check performance of a classification model built using sklearn\n",
    "def model_performance_classification_sklearn(model, predictors, target):\n",
    "    \"\"\"\n",
    "    Function to compute different metrics to check classification model performance\n",
    "\n",
    "    model: classifier\n",
    "    predictors: independent variables\n",
    "    target: dependent variable\n",
    "    \"\"\"\n",
    "\n",
    "    TP = confusion_matrix(target, model.predict(predictors))[1, 1]\n",
    "    FP = confusion_matrix(target, model.predict(predictors))[0, 1]\n",
    "    FN = confusion_matrix(target, model.predict(predictors))[1, 0]\n",
    "\n",
    "    # predicting using the independent variables\n",
    "    pred = model.predict(predictors)\n",
    "\n",
    "    acc = accuracy_score(target, pred)  # to compute Accuracy\n",
    "    recall = recall_score(target, pred)  # to compute Recall\n",
    "    precision = precision_score(target, pred)  # to compute Precision\n",
    "    f1 = f1_score(target, pred)  # to compute F1-score\n",
    "\n",
    "    # creating a dataframe of metrics\n",
    "    df_perf = pd.DataFrame(\n",
    "        {\n",
    "            \"Accuracy\": acc,\n",
    "            \"Recall\": recall,\n",
    "            \"Precision\": precision,\n",
    "            \"F1\": f1\n",
    "        },\n",
    "        index=[0],\n",
    "    )\n",
    "\n",
    "    return df_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_(style):\n",
    "    if style == 0:\n",
    "        return \"Modern\"\n",
    "    elif style == 1:\n",
    "        return \"Traditional\"\n",
    "    else:\n",
    "        return \"Invalid value\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8k_PJhlqv2BT",
    "tags": []
   },
   "source": [
    "# Building dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv \n",
    "data_polder_30_16 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_30_16.csv')\n",
    "data_polder_30_17 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_30_17.csv')\n",
    "data_polder_30_18 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_30_18.csv')\n",
    "\n",
    "data_polder_22_21 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_22_21.csv')\n",
    "data_polder_29_21 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_29_21.csv')\n",
    "data_polder_30_21 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_30_21.csv')\n",
    "data_polder_34_21 = pd.read_csv('D:\\crop_season_stats\\data_transfer\\polder_34_21.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Polder</th>\n",
       "      <th>pred</th>\n",
       "      <th>Total_Lenght</th>\n",
       "      <th>obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2515976.117</td>\n",
       "      <td>757546.2025</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>126</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516122.841</td>\n",
       "      <td>756392.5778</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>112</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516187.416</td>\n",
       "      <td>755620.6457</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>118</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516045.302</td>\n",
       "      <td>756393.8842</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>87</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516111.245</td>\n",
       "      <td>756361.9392</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>120</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513306.118</td>\n",
       "      <td>756254.9576</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>82</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513306.118</td>\n",
       "      <td>756254.9576</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>82</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513264.922</td>\n",
       "      <td>756440.6890</td>\n",
       "      <td>30</td>\n",
       "      <td>Traditional</td>\n",
       "      <td>156</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513297.980</td>\n",
       "      <td>756429.8527</td>\n",
       "      <td>30</td>\n",
       "      <td>Traditional</td>\n",
       "      <td>158</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513100.151</td>\n",
       "      <td>756525.7023</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>67</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>428 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    Crop     Latitude    Longitude  Polder         pred  \\\n",
       "0    2016  Modern  2515976.117  757546.2025      30       Modern   \n",
       "1    2016  Modern  2516122.841  756392.5778      30       Modern   \n",
       "2    2016  Modern  2516187.416  755620.6457      30       Modern   \n",
       "3    2016  Modern  2516045.302  756393.8842      30       Modern   \n",
       "4    2016  Modern  2516111.245  756361.9392      30       Modern   \n",
       "..    ...     ...          ...          ...     ...          ...   \n",
       "138  2018  Modern  2513306.118  756254.9576      30       Modern   \n",
       "139  2018  Modern  2513306.118  756254.9576      30       Modern   \n",
       "140  2018  Modern  2513264.922  756440.6890      30  Traditional   \n",
       "141  2018  Modern  2513297.980  756429.8527      30  Traditional   \n",
       "142  2018  Modern  2513100.151  756525.7023      30       Modern   \n",
       "\n",
       "     Total_Lenght     obs  \n",
       "0             126  Modern  \n",
       "1             112  Modern  \n",
       "2             118  Modern  \n",
       "3              87  Modern  \n",
       "4             120  Modern  \n",
       "..            ...     ...  \n",
       "138            82  Modern  \n",
       "139            82  Modern  \n",
       "140           156  Modern  \n",
       "141           158  Modern  \n",
       "142            67  Modern  \n",
       "\n",
       "[428 rows x 8 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_polder_30 = pd.concat([data_polder_30_16,\n",
    "                            data_polder_30_17,\n",
    "                            data_polder_30_18])\n",
    "\n",
    "data_polder_30['obs'] = data_polder_30['Crop']\n",
    "data_polder_30['pred'] = data_polder_30['pred'].apply(convert_)\n",
    "data_polder_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <td>Modern</td>\n",
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       "      <td>106</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polder  Longitude  Latitude  Total_Leng     obs  class_pred  Total_Lenght  \\\n",
       "0       22    89.4247   22.6234         112  Modern           0           112   \n",
       "1       22    89.4254   22.6243         104  Modern           0           104   \n",
       "2       22    89.4465   22.6312         103  Modern           0           103   \n",
       "3       22    89.4506   22.6269         149  Modern           1           149   \n",
       "4       22    89.4504   22.6270         145  Modern           1           145   \n",
       "..     ...        ...       ...         ...     ...         ...           ...   \n",
       "106  34/2P    89.5468   22.6751          62  Modern           0            62   \n",
       "107  34/2P    89.5448   22.6770         109  Modern           0           109   \n",
       "108  34/2P    89.5489   22.6752         109  Modern           0           109   \n",
       "109  34/2P    89.5462   22.6787          99  Modern           0            99   \n",
       "110  34/2P    89.5431   22.6803         106  Modern           0           106   \n",
       "\n",
       "            pred  \n",
       "0         Modern  \n",
       "1         Modern  \n",
       "2         Modern  \n",
       "3    Traditional  \n",
       "4    Traditional  \n",
       "..           ...  \n",
       "106       Modern  \n",
       "107       Modern  \n",
       "108       Modern  \n",
       "109       Modern  \n",
       "110       Modern  \n",
       "\n",
       "[333 rows x 8 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_polder_2021 = pd.concat([data_polder_22_21,\n",
    "                              data_polder_29_21,\n",
    "                              data_polder_30_21,\n",
    "                              data_polder_34_21])\n",
    "\n",
    "data_polder_2021['obs'] = data_polder_2021['obs']\n",
    "data_polder_2021['pred'] = data_polder_2021['class_pred'].apply(convert_)\n",
    "data_polder_2021                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Polder</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Total_Lenght</th>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>34/2P</td>\n",
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       "      <td>62</td>\n",
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       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
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       "      <td>22.6770</td>\n",
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       "      <th>108</th>\n",
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       "      <td>22.6752</td>\n",
       "      <td>109</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>34/2P</td>\n",
       "      <td>89.5462</td>\n",
       "      <td>22.6787</td>\n",
       "      <td>99</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>34/2P</td>\n",
       "      <td>89.5431</td>\n",
       "      <td>22.6803</td>\n",
       "      <td>106</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polder  Longitude  Latitude  Total_Lenght     obs         pred\n",
       "0       22    89.4247   22.6234           112  Modern       Modern\n",
       "1       22    89.4254   22.6243           104  Modern       Modern\n",
       "2       22    89.4465   22.6312           103  Modern       Modern\n",
       "3       22    89.4506   22.6269           149  Modern  Traditional\n",
       "4       22    89.4504   22.6270           145  Modern  Traditional\n",
       "..     ...        ...       ...           ...     ...          ...\n",
       "106  34/2P    89.5468   22.6751            62  Modern       Modern\n",
       "107  34/2P    89.5448   22.6770           109  Modern       Modern\n",
       "108  34/2P    89.5489   22.6752           109  Modern       Modern\n",
       "109  34/2P    89.5462   22.6787            99  Modern       Modern\n",
       "110  34/2P    89.5431   22.6803           106  Modern       Modern\n",
       "\n",
       "[333 rows x 6 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_polder_2021 = pd.DataFrame(data_polder_2021['Polder'])\n",
    "df_polder_2021['Longitude'] = data_polder_2021['Longitude']\n",
    "df_polder_2021['Latitude'] = data_polder_2021['Latitude']\n",
    "df_polder_2021['Total_Lenght'] = data_polder_2021['Total_Lenght']\n",
    "df_polder_2021['obs'] = data_polder_2021['obs']\n",
    "df_polder_2021['pred'] = data_polder_2021['pred']\n",
    "df_polder_2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-07 14:09:59,583 - distributed.scheduler - WARNING - Worker failed to heartbeat within 300 seconds. Closing: <WorkerState 'tcp://127.0.0.1:58379', name: 2, status: running, memory: 0, processing: 0>\n",
      "2023-02-07 14:10:01,615 - distributed.scheduler - WARNING - Worker failed to heartbeat within 300 seconds. Closing: <WorkerState 'tcp://127.0.0.1:58383', name: 1, status: running, memory: 0, processing: 0>\n",
      "2023-02-07 14:10:01,619 - distributed.scheduler - WARNING - Received heartbeat from unregistered worker 'tcp://127.0.0.1:58383'.\n",
      "2023-02-07 14:10:01,627 - distributed.scheduler - WARNING - Received heartbeat from unregistered worker 'tcp://127.0.0.1:58379'.\n",
      "2023-02-07 14:10:05,110 - distributed.nanny - WARNING - Restarting worker\n"
     ]
    }
   ],
   "source": [
    "df_polder_2021.Total_Lenght.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_polder_2021.to_excel('D:\\crop_season_stats\\data_transfer\\data_2021.xlsx', index=True, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Polder stat 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([22, 29, 30, '34/2P'])\n"
     ]
    }
   ],
   "source": [
    "polder_stat_2021 = df_polder_2021.groupby('Polder')\n",
    "print(polder_stat_2021.groups.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_2021_22 =  pd.DataFrame(polder_stat_2021.get_group(22))\n",
    "polder_stat_2021_29 =  pd.DataFrame(polder_stat_2021.get_group(29))\n",
    "polder_stat_2021_30 =  pd.DataFrame(polder_stat_2021.get_group(30))\n",
    "polder_stat_2021_34 =  pd.DataFrame(polder_stat_2021.get_group('34/2P'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_22 = polder_stat_2021_22.groupby(polder_stat_2021_22['obs'])\n",
    "polder_22_df_stat_mode = pd.DataFrame(polder_stat_22.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_29 = polder_stat_2021_29.groupby(polder_stat_2021_29['obs'])\n",
    "polder_29_df_stat_mode = pd.DataFrame(polder_stat_29.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30 = polder_stat_2021_30.groupby(polder_stat_2021_30['obs'])\n",
    "polder_30_df_stat_mode = pd.DataFrame(polder_stat_30.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_34 = polder_stat_2021_34.groupby(polder_stat_2021_34['obs'])\n",
    "polder_34_df_stat_mode = pd.DataFrame(polder_stat_34.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Modern</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Polder</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2021</th>\n",
       "      <th>22</th>\n",
       "      <td>110</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>103</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>124</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>114</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modern  std\n",
       "Year Polder             \n",
       "2021 22         110   19\n",
       "     29         103   27\n",
       "     30         124   25\n",
       "     34         114   29"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [\n",
    "    \n",
    "    [\"2021\"],\n",
    "    [\"22\", \"29\", \"30\", \"34\"]\n",
    "]\n",
    "\n",
    "tuples = list(zip(*labels))\n",
    "index = pd.MultiIndex.from_product(labels, names=[\"Year\", \"Polder\"])\n",
    "\n",
    "df_stats = pd.DataFrame([\n",
    "    [polder_22_df_stat_mode.Total_Lenght.mean(),polder_22_df_stat_mode.Total_Lenght.std()],\n",
    "    [polder_29_df_stat_mode.Total_Lenght.mean(),polder_29_df_stat_mode.Total_Lenght.std()],\n",
    "    [polder_30_df_stat_mode.Total_Lenght.mean(),polder_30_df_stat_mode.Total_Lenght.std()],\n",
    "    [polder_34_df_stat_mode.Total_Lenght.mean(),polder_34_df_stat_mode.Total_Lenght.std()],\n",
    "\n",
    "],\n",
    "    \n",
    "    index=index,columns=[\"Modern\", \"std\"])\n",
    "\n",
    "df_stats = df_stats.astype(int)\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_report_22_21 = classification_report_to_dataframe(classification_report(polder_22_df_stat_mode.obs,\n",
    "                                                                        polder_22_df_stat_mode.pred))\n",
    "\n",
    "class_report_29_21 = classification_report_to_dataframe(classification_report(polder_29_df_stat_mode.obs,\n",
    "                                                                        polder_29_df_stat_mode.pred))\n",
    "\n",
    "class_report_30_21 = classification_report_to_dataframe(classification_report(polder_30_df_stat_mode.obs,\n",
    "                                                                        polder_30_df_stat_mode.pred))\n",
    "\n",
    "class_report_34_21 = classification_report_to_dataframe(classification_report(polder_34_df_stat_mode.obs,\n",
    "                                                                        polder_34_df_stat_mode.pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                precision recall f1-score support\n",
       " 0        Modern      1.00   0.87     0.93      47\n",
       " 1   Traditional      0.00   0.00     0.00       0\n",
       " 2      accuracy       NaN    NaN     0.87      47\n",
       " 3     macro avg      0.50   0.44     0.47      47\n",
       " 4  weighted avg      1.00   0.87     0.93      47,\n",
       "                 precision recall f1-score support\n",
       " 0        Modern      1.00   0.87     0.93     126\n",
       " 1   Traditional      0.00   0.00     0.00       0\n",
       " 2      accuracy       NaN    NaN     0.87     126\n",
       " 3     macro avg      0.50   0.43     0.46     126\n",
       " 4  weighted avg      1.00   0.87     0.93     126,\n",
       "                 precision recall f1-score support\n",
       " 0        Modern      1.00   0.59     0.74      49\n",
       " 1   Traditional      0.00   0.00     0.00       0\n",
       " 2      accuracy       NaN    NaN     0.59      49\n",
       " 3     macro avg      0.50   0.30     0.37      49\n",
       " 4  weighted avg      1.00   0.59     0.74      49,\n",
       "                 precision recall f1-score support\n",
       " 0        Modern      1.00   0.68     0.81     111\n",
       " 1   Traditional      0.00   0.00     0.00       0\n",
       " 2      accuracy       NaN    NaN     0.68     111\n",
       " 3     macro avg      0.50   0.34     0.41     111\n",
       " 4  weighted avg      1.00   0.68     0.81     111)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_report_22_21, class_report_29_21, class_report_30_21, class_report_34_21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Polder stat 16-18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Polder</th>\n",
       "      <th>pred</th>\n",
       "      <th>Total_Lenght</th>\n",
       "      <th>obs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2515976.117</td>\n",
       "      <td>757546.2025</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>126</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516122.841</td>\n",
       "      <td>756392.5778</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>112</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516187.416</td>\n",
       "      <td>755620.6457</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>118</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516045.302</td>\n",
       "      <td>756393.8842</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>87</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2516111.245</td>\n",
       "      <td>756361.9392</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>120</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513306.118</td>\n",
       "      <td>756254.9576</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>82</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513306.118</td>\n",
       "      <td>756254.9576</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>82</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513264.922</td>\n",
       "      <td>756440.6890</td>\n",
       "      <td>30</td>\n",
       "      <td>Traditional</td>\n",
       "      <td>156</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513297.980</td>\n",
       "      <td>756429.8527</td>\n",
       "      <td>30</td>\n",
       "      <td>Traditional</td>\n",
       "      <td>158</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2018</td>\n",
       "      <td>Modern</td>\n",
       "      <td>2513100.151</td>\n",
       "      <td>756525.7023</td>\n",
       "      <td>30</td>\n",
       "      <td>Modern</td>\n",
       "      <td>67</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>428 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year    Crop     Latitude    Longitude  Polder         pred  \\\n",
       "0    2016  Modern  2515976.117  757546.2025      30       Modern   \n",
       "1    2016  Modern  2516122.841  756392.5778      30       Modern   \n",
       "2    2016  Modern  2516187.416  755620.6457      30       Modern   \n",
       "3    2016  Modern  2516045.302  756393.8842      30       Modern   \n",
       "4    2016  Modern  2516111.245  756361.9392      30       Modern   \n",
       "..    ...     ...          ...          ...     ...          ...   \n",
       "138  2018  Modern  2513306.118  756254.9576      30       Modern   \n",
       "139  2018  Modern  2513306.118  756254.9576      30       Modern   \n",
       "140  2018  Modern  2513264.922  756440.6890      30  Traditional   \n",
       "141  2018  Modern  2513297.980  756429.8527      30  Traditional   \n",
       "142  2018  Modern  2513100.151  756525.7023      30       Modern   \n",
       "\n",
       "     Total_Lenght     obs  \n",
       "0             126  Modern  \n",
       "1             112  Modern  \n",
       "2             118  Modern  \n",
       "3              87  Modern  \n",
       "4             120  Modern  \n",
       "..            ...     ...  \n",
       "138            82  Modern  \n",
       "139            82  Modern  \n",
       "140           156  Modern  \n",
       "141           158  Modern  \n",
       "142            67  Modern  \n",
       "\n",
       "[428 rows x 8 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_polder_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_polder_30.to_excel('D:\\crop_season_stats\\data_transfer\\data_30_16_18.xlsx', index=True, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([2016, 2017, 2018])\n"
     ]
    }
   ],
   "source": [
    "polder_stat_polder_30= data_polder_30.groupby('Year')\n",
    "print(polder_stat_polder_30.groups.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30_16 =  pd.DataFrame(polder_stat_polder_30.get_group(2016))\n",
    "polder_stat_30_17 =  pd.DataFrame(polder_stat_polder_30.get_group(2017))\n",
    "polder_stat_30_18 =  pd.DataFrame(polder_stat_polder_30.get_group(2018))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30_16 = polder_stat_30_16.groupby(polder_stat_30_16['obs'])\n",
    "polder_30_16_df_stat_trad = pd.DataFrame(polder_stat_30_16.get_group('Traditional'))\n",
    "polder_30_16_df_stat_mode = pd.DataFrame(polder_stat_30_16.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30_17 = polder_stat_30_17.groupby(polder_stat_30_17['obs'])\n",
    "polder_30_17_df_stat_trad = pd.DataFrame(polder_stat_30_17.get_group('Traditional'))\n",
    "polder_30_17_df_stat_mode = pd.DataFrame(polder_stat_30_17.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30_18 = polder_stat_30_18.groupby(polder_stat_30_18['obs'])\n",
    "polder_30_18_df_stat_mode = pd.DataFrame(polder_stat_30_18.get_group('Modern'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Traditional</th>\n",
       "      <th>std</th>\n",
       "      <th>Modern</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Polder</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <th>30</th>\n",
       "      <td>150</td>\n",
       "      <td>27</td>\n",
       "      <td>125</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <th>30</th>\n",
       "      <td>143</td>\n",
       "      <td>26</td>\n",
       "      <td>120</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Traditional  std  Modern  std\n",
       "Year Polder                               \n",
       "2016 30              150   27     125   30\n",
       "2017 30              143   26     120   26\n",
       "2018 30                0    0     117   31"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = [\n",
    "    \n",
    "    [\"2016\",\"2017\",\"2018\"],\n",
    "    [\"30\"]\n",
    "]\n",
    "\n",
    "tuples = list(zip(*labels))\n",
    "index = pd.MultiIndex.from_product(labels, names=[\"Year\", \"Polder\"])\n",
    "\n",
    "df_stats = pd.DataFrame([\n",
    "    \n",
    "    [polder_30_16_df_stat_trad.Total_Lenght.mean(),polder_30_16_df_stat_trad.Total_Lenght.std(),polder_30_16_df_stat_mode.Total_Lenght.mean(),polder_30_16_df_stat_mode.Total_Lenght.std()],\n",
    "     [polder_30_17_df_stat_trad.Total_Lenght.mean(),polder_30_17_df_stat_trad.Total_Lenght.std(),polder_30_17_df_stat_mode.Total_Lenght.mean(),polder_30_17_df_stat_mode.Total_Lenght.std()],\n",
    "     \n",
    "    [0,0,polder_30_18_df_stat_mode.Total_Lenght.mean(),polder_30_18_df_stat_mode.Total_Lenght.std()]\n",
    "\n",
    "],\n",
    "    \n",
    "    index=index,columns=[\"Traditional\", \"std\",\"Modern\", \"std\"])\n",
    "\n",
    "df_stats = df_stats.astype(int)\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_stat_30_16_df = pd.DataFrame(polder_stat_30_16)\n",
    "polder_stat_30_17_df = pd.DataFrame(polder_stat_30_17)\n",
    "polder_stat_30_18_df = pd.DataFrame(polder_stat_30_18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_report_30_16  = classification_report_to_dataframe(classification_report(polder_stat_30_16_df.obs,\n",
    "                                                                        polder_stat_30_16_df.pred))\n",
    "\n",
    "class_report_30_17 = classification_report_to_dataframe(classification_report(polder_stat_30_17.obs,\n",
    "                                                                        polder_stat_30_17.pred))\n",
    "\n",
    "class_report_30_18 = classification_report_to_dataframe(classification_report(polder_stat_30_18.obs,\n",
    "                                                                        polder_stat_30_18.pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                precision recall f1-score support\n",
       " 0        Modern      0.90   0.73     0.81     100\n",
       " 1   Traditional      0.53   0.79     0.63      38\n",
       " 2      accuracy       NaN    NaN     0.75     138\n",
       " 3     macro avg      0.71   0.76     0.72     138\n",
       " 4  weighted avg      0.80   0.75     0.76     138,\n",
       "                 precision recall f1-score support\n",
       " 0        Modern      0.79   0.70     0.74      86\n",
       " 1   Traditional      0.63   0.74     0.68      61\n",
       " 2      accuracy       NaN    NaN     0.71     147\n",
       " 3     macro avg      0.71   0.72     0.71     147\n",
       " 4  weighted avg      0.72   0.71     0.72     147,\n",
       "                 precision recall f1-score support\n",
       " 0        Modern      1.00   0.77     0.87     143\n",
       " 1   Traditional      0.00   0.00     0.00       0\n",
       " 2      accuracy       NaN    NaN     0.77     143\n",
       " 3     macro avg      0.50   0.38     0.43     143\n",
       " 4  weighted avg      1.00   0.77     0.87     143)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_report_30_16, class_report_30_17, class_report_30_18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2021 = pd.read_excel('D:\\crop_season_stats\\data_transfer\\data_2021.xlsx')\n",
    "df_2016_18 = pd.read_excel('D:\\crop_season_stats\\data_transfer\\data_30_16_18.xlsx')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([22, 29, 30, '34/2P'])\n"
     ]
    }
   ],
   "source": [
    "polder_stat_2021 = df_2021.groupby('Polder')\n",
    "print(polder_stat_2021.groups.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_den_2021_22 =  pd.DataFrame(polder_stat_2021.get_group(22))\n",
    "polder_den_2021_29 =  pd.DataFrame(polder_stat_2021.get_group(29))\n",
    "polder_den_2021_30 =  pd.DataFrame(polder_stat_2021.get_group(30))\n",
    "polder_den_2021_34 =  pd.DataFrame(polder_stat_2021.get_group('34/2P'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Polder</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Total_Lenght</th>\n",
       "      <th>obs</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>89.4247</td>\n",
       "      <td>22.6234</td>\n",
       "      <td>112</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>89.4254</td>\n",
       "      <td>22.6243</td>\n",
       "      <td>104</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>89.4465</td>\n",
       "      <td>22.6312</td>\n",
       "      <td>103</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Modern</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>89.4506</td>\n",
       "      <td>22.6269</td>\n",
       "      <td>149</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Traditional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>89.4504</td>\n",
       "      <td>22.6270</td>\n",
       "      <td>145</td>\n",
       "      <td>Modern</td>\n",
       "      <td>Traditional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Polder  Longitude  Latitude  Total_Lenght     obs         pred\n",
       "0           0     22    89.4247   22.6234           112  Modern       Modern\n",
       "1           1     22    89.4254   22.6243           104  Modern       Modern\n",
       "2           2     22    89.4465   22.6312           103  Modern       Modern\n",
       "3           3     22    89.4506   22.6269           149  Modern  Traditional\n",
       "4           4     22    89.4504   22.6270           145  Modern  Traditional"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polder_den_2021_22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.set_context(\"paper\")\n",
    "\n",
    "SMALL_SIZE = 10\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=SMALL_SIZE)  # fontsize of the figure title\n",
    "\n",
    "# create figure\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(13,6))\n",
    "\n",
    "# Polder 22\n",
    "ax1 = sns.histplot(data= polder_den_2021_22, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax1)\n",
    "ax1.set_title('Polder 22',fontweight =\"bold\")\n",
    "ax1.set_xlabel(\"Accumulated DOY\")\n",
    "ax1.set_ylabel(\"Number of Samples\")\n",
    "ax1.set_ylim(0, 25)\n",
    "ax1.set_xlim(60, 220)\n",
    "\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "# Polder 29\n",
    "ax2 = sns.histplot(data= polder_den_2021_29, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax2)\n",
    "ax2.set_title('Polder 29',fontweight =\"bold\")\n",
    "ax2.set_xlabel(\"Accumulated DOY\")\n",
    "ax2.set_ylabel(\"Number of Samples\")\n",
    "ax2.legend_ = None\n",
    "ax2.set_ylim(0, 25)\n",
    "ax2.set_xlim(60, 220)\n",
    "\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "# Polder 30\n",
    "ax3 = sns.histplot(data= polder_den_2021_30, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax3)\n",
    "ax3.set_title('Polder 30',fontweight =\"bold\")\n",
    "ax3.set_xlabel(\"Accumulated DOY\")\n",
    "ax3.set_ylabel(\"Number of Samples\")\n",
    "ax3.legend_ = None\n",
    "ax3.set_ylim(0, 25)\n",
    "ax3.set_xlim(60, 220)\n",
    "\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "# Polder 34\n",
    "ax4 = sns.histplot(data= polder_den_2021_34, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax4)\n",
    "ax4.set_title('Polder 34',fontweight =\"bold\")\n",
    "ax4.set_xlabel(\"Accumulated DOY\")\n",
    "ax4.set_ylabel(\"Number of Samples\")\n",
    "ax4.legend_ = None\n",
    "ax4.set_ylim(0, 25)\n",
    "ax4.set_xlim(60, 220)\n",
    "\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "\n",
    "fig.tight_layout(pad=1)\n",
    "\n",
    "plt.rc('legend', loc=\"upper right\")\n",
    "plt.savefig(\"D:\\crop_season_stats\\data\\2021_polders.svg\", format='svg')\n",
    "\n",
    "#Total_Lenght"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16-18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([2016, 2017, 2018])\n"
     ]
    }
   ],
   "source": [
    "polder_stat_2016_18 = df_2016_18.groupby('Year')\n",
    "print(polder_stat_2016_18.groups.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "polder_den_2016 =  pd.DataFrame(polder_stat_2016_18.get_group(2016))\n",
    "polder_den_2017 =  pd.DataFrame(polder_stat_2016_18.get_group(2017))\n",
    "polder_den_2018 =  pd.DataFrame(polder_stat_2016_18.get_group(2018))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RI0fy4Ycf8te//pWamhq8Xi8ul4vk5OQYPxoRERERkd5DuySLiIhIXEpMTOT222/nn//8J8899xxut5tjjz2WGTNm8Le//Y399tuPf/7znxQVFTFmzBhuueUWLBYLZ5xxBpWVlZx11lkEAgFGjhzJ9ddfH+uHIyIiIiLSa6hhKCIiInFr+PDh3HPPPdjtdkKhUMvl1113Xav3cTqdXHPNNT0RnoiIiIhIn6QpySIiIiIiIiIiItJCDUMRERERERERERFpoYahiIiIiIiIiIiItFDDUERERERERERERFrEpGH4xRdfcPLJJzNhwgSmTJnCzTffTFNTEwCLFi3i5JNPZvz48RxyyCG8+OKLsQhRRERERLqJakERERGR+NbjDcOqqiouuOACTj31VL755htmzZrFV199xcMPP0xtbS3nn38+xx9/PF9//TW33HILt956K4sXL+7pMEVERESkG6gWFBEREYl/tp4+YVpaGp9//jlerxfTNKmpqSEQCJCWlsZ7771HSkoKp512GgD77rsvxx57LM888wzjxo3r6VBFREREpIupFhQRERGJfzGZkuz1egE46KCDOPbYY8nMzOTEE09k1apVjBgxYrvbFhYWsnz58liEKSIiIiLdQLWgiIiISHzr8RGGP/Xee+9RW1vLVVddxWWXXUZ2djZut3u727hcLhoaGjp9DsMwMAxjV0PtU7blQ3nZOeWndcpN25Sftik/reuu3Pyv4/XUz8I0zR45T3+ws7qmN7+mVAvGht6P26b8tE65aZvy0zblp3XKTduUn7Z1V15i2jB0uVy4XC6uvvpqTj75ZM444wzq6+u3u01TUxMej6fT50hPT9/VMPus7OzsWIcQ15Sf1ik3bVN+2qb8tK6rcxOJRKiursZut2OxbD+pIDklFbvN2qXna00oHKG2prrdt58yZQoAzz77LIMGDWq53G638/zzz/OPf/yDc845h3PPPbfDsXz77bdceumlfPbZZx2+byxFo1GsVivZ2dlYrT3zc+sJqgVjS+/HbVN+WqfctE35aZvy0zrlpm3KT8/q8Ybht99+y/XXX8/rr7+Ow+EAIBgMYrfbKSws3KGAX716NcOHD+/0+SorKwmFQrsUc19jGAbZ2dmUlpZq1MdOKD+tU27apvy0TflpXXflJhqNEolECIVC2zUMDcPAbrNyz9uLCYQjXXa+nXHarFwxfRzhcLhDjy05OZk333yT888/H2huFoZCId588008Hk/L4+qocDgM0Otqg20/y9LS0h2av3a7vVc1xVQLxp7ej9um/LROuWmb8tM25ad1yk3blJ+2dVct2OMNw5EjR9LU1MRdd93FzJkzKS8v529/+xsnnXQSRxxxBHfddRdPPPEEp512GgsWLOCNN97ggQce6PT5TNPUE6oVyk3blJ/WKTdtU37apvy0rqtz87+OFQhHCIajXXa+rnTYYYfx3nvvcd5557U0yJYtW0Y4HKawsLDldtFolOeee4633nqL2tpaCgoKOOecc5g4cSLQ3Cy66667+O6770hOTuaQQw7Z7jybN2/m/vvvZ+nSpbjdbqZNm8ZZZ52F3W7n7bffZtasWSQlJbFs2TKuvPJK3njjDUaPHs3333/PypUrycrK4uyzz+bggw/ukbzs7DnS215PqgXjh3LTNuWndcpN25Sftik/rVNu2qb87Fx35aTHNz3xeDw88sgjrFq1iilTpnDGGWew3377cf3115Oamspjjz3GO++8w6RJk7jhhhu44YYbmDx5ck+HKSIiIjG07777Eg6HWbBgQctls2fP5qijjtrudv/+97959dVXufHGG3nttdeYMWMGv//971s2yfjzn/+M1WrlpZde4t5772X+/Pkt921sbOS3v/0tQ4YM4cUXX+Qf//gHCxYs4PHHH2+5zcqVKznssMOYNWsWBxxwAABvvvkml156KW+88QYHHnggd955J4FAoDvT0aeoFhQRERGJfzFZw7CwsJDHHntsp9eNHTuW559/vocjEhERkXhitVo57LDDeOedd9hnn30IBALMnTuXxx9/nC+//LLldm+//TannXZay866Bx98MHPnzuWtt94iJSWFxYsX8/TTT5OQkEBCQgK/+tWvuOGGGwD48ssvCYfD/PrXv8YwDLKysjjnnHP405/+tN1U6MMPP3y7acAHHXRQyxTZI488kqeffpqamhqtq9MBqgVFRERE4ltMNz0RERERac306dP5zW9+g9/v58svv2Ts2LE7rM9SXV1Nbm7udpfl5uayevVqKioqAMjKymq5Li8vr+XfW7Zsobq6mmOOOWa7+4dCIaqrmzdpSUtL22HNwLS0tJZ/b9uAJBqNz6ndIiIiIiKdoYahiIiIxKVhw4YxcOBAPv74Yz766CN+/vOf73CbnJwciouLt7ts8+bNpKenk5mZCUBJSUnLbsvl5eUtt8vMzCQ/P5+nnnqq5bKGhgaqqqpISUnphkckIiIiItI79PgahiIiIiLtNX36dF566SU2btzIpEmTdrj+6KOP5tlnn2XlypVEIhHmzJnD559/zpFHHkl2djb77LMP//znP6mvr6eyspInnnii5b777rsvjY2NPP/88wSDQerr6/nrX//KTTfdhGEYPfgoRURERETii0YYioiI9FNOmzXuz3HooYfy4IMP8otf/AKbbcey5eSTTyYSifDnP/+ZyspKBgwYwB//+Ef23HNPAP7whz9w9913M2PGDBISEpg+fTrLli0DmjffuOuuu3jggQd47rnniEajjB8/nltuuWWXYhYRERER6e0Ms4/vSV1RUUEoFIp1GHHFMAxycnLYsmWLtiTfCeWndcpN25Sftik/reuu3ESjUSorK0lPT99hHb6MzEzsO2nAdYdQOEzFT6YCd4bdbu/Xv8/b+lna7XYyMjJiFFn8Uy24I70ft035aZ1y0zblp23KT+uUm7YpP23rrlpQIwxFRET6oYry8h6bdqvCTkRERESkd1HDUEREpJ9SI09ERERERHZGm56IiIiIiIiIiIhICzUMRUREREREREREpIUahiIiIiIiIiIiItJCDUMRERERERERERFpoYahiIiIiIiIiIiItFDDUERERERERERERFrsUsNwzZo1lJaWdlUsIiIi0oMMw+iRL+m7VAuKiIiI9E22jtz422+/5aabbuLVV1/l+eef58Ybb8Rms3HPPfdw2GGHdVeMIiIi0sWyMtOx2hw9cq5IOEhZeWWPnKsrbNq0iQEDBsQ6jLikWlBERESkf+hQw/Cuu+5i6tSpmKbJQw89xG233UZKSgp33XWXikQREZFewjAMrDYHvvdvwwwHuvdcNifeaddiGAamabbrPnfddRfvv/8+AJFIhHA4jMvlarn/7bffzrhx4zodU0lJCaeeeirPPfccubm5HHnkkS3HfPDBB6mpqeG6664D2O667nTrrbcCtJw3XqkWFBEREekfOtQwXLt2LU8//TRr166loqKCo446CofDwZVXXtld8YmIiEg3McMB6OaGYftahNubOXMmM2fOBODtt9/miSee4JVXXiEUCnVtcFu98847Lf+uqalp9TpRLSgiIiLSX3SoYWi1WvH7/XzyySfsueeeOBwONm/ejNfr7a74RERERFpsGx34i1/8gtmzZ3PYYYdx8cUX88gjj/DFF19QXl6O0+nk4IMP5rLLLsMwDBoaGrj33nuZN28ebrebn/3sZ9sdc+rUqdx9990sWbKEDz74AIDVq1fz6KOPtlw3fvx4amtr+b//+z8+//xzwuEwu+++O5dccgkDBgxoievqq6/mySefxOfzMWrUKK655hqysrIwTZPnnnuO999/n7KyMgzDYPLkyVx99dU4nc5YpLJTVAuKiIiI9A8dahgedthhnH766WzevJkbbriB1atXc/HFF3PMMcd0V3wiIiIiO2hoaGDWrFk0NTXx0ksvMX/+fO6++27S09NZunQpl112Gfvvvz977bUX99xzD5s3b+bpp5/GYrFw88037/SYZ555Jps3bwZ2PjX4j3/8I1arlf/7v//D6/Xy6KOP8tvf/pbHH3+85TZffPEFjzzyCKFQiKuuuoqnnnqKmTNnMmfOHF566SXuu+8+BgwYwIYNG7j00kv54IMPOProo7snSd1AtaCIiIhI/9ChhuEf/vAHXnvtNVwuF0cddRTr16/nlFNO4cwzz+yu+ERERER2cMQRR2C327Hb7RxzzDEcccQRpKamUllZSSAQICEhgYqKCoLBIB9//DG33norqampAFx44YWcd955HTpfcXExixYt4oknniA9PR2ACy64gA8++IAvv/yS3XffHYBTTz2VxMREAPbbbz+WLl0KwKRJkxg9ejRZWVnU1NRQW1tLcnIyFRUVXZWSHqFaUERERKR/6PCU5BNPPJHa2lq+//57dt99d0477TSsVmt3xSciIiKyg4yMjJZ/NzY2cu+997Jo0SIyMzMZMWIEpmkSjUapq6sjFAqRlZXVcvu8vLwOn6+6uhqA3NzclsusVivZ2dls2bKlpWGYlpa23fXRaBSAaDTKo48+yueff05KSgqFhYUEg8GW63sL1YIiIiIi/YOlIzf2+/3MnDmTSZMmcfrpp7N+/XqmTZvG2rVruys+ERERkTbdeeeduN1uXn75ZR5//HGuueaalh2Vk5OTcTgclJSUtNy+vLy8w+fIyckBmkcabhOJRNiyZUvLiMO2PPzww5SWlvL888/z1FNP8ac//YmEhIQOxxFrqgVFRERE+ocONQxvv/12GhoaePvtt7Hb7RQUFHDwwQdzyy23dOiky5cv5+yzz2bixIlMmTKF3/3ud1RVVQHwpz/9iTFjxjB+/PiWrxdeeKFDxxcREZH+w+/343A4sFqtNDQ08K9//Qu/3084HMZut3PEEUfw6KOPUlZWhs/n46GHHmr1WA6HA7/fv8Pl6enpTJ48mX/84x8t054feughotEo++23X4diDAQCvPDCC6xbt45wOLxLj72nqRYUERER6R861DD8+OOPue222xgyZAiGYWC327n22mtZsmRJu4/R1NTEeeedx/jx45k3bx5vvvkmNTU1XH/99QAsWbKEm2++mYULF7Z8zZgxo2OPSkRERP4nw+aEbv4ybN2/A/Bll13G6tWrOeaYYzjjjDPw+/1MnDixZdTbJZdcwm677cY555zD6aefzm677dbqsQ455BC+//57fvGLX+xw3fXXX09ubi7nn38+J5xwAmvXruXuu+8mKSnpf8Z47rnnEggEOP744znllFP44YcfOPzww3vdyDzVgiIiIiL9Q4fWMIxGozgcDoCWqT4/vaw9iouLGTVqFBdffDFWqxWHw8GMGTP43e9+RzAYZOXKlYwZM6YjYYmIiEgHmKZJJBzEO+3aHjlfJBxsqRs6avr06UyfPr3l/7m5ucyZM2e724wcOZKHH3641WM4nU6uvPJKrrzyypbLzjjjjJZ///R448eP59VXX93pdUlJSVx99dU7PcfO4jr77LNb/p2Xl8e9997baow725U5HqkWFBEREekfOtQwnDx5MjfddBN//OMfMQwDgHvuuYeJEye2+xhDhw7lkUce2e6yd999l9GjR7N8+XLC4TD33XcfCxYsIDExkZ///Oecd955WCwdGgzZwjCMllil2bZ8KC87p/y0Trlpm/LTNuWndd2Vm7aOV1Ze2WM/i842C2VHO6trevI1pVqwb9D7cduUn9YpN21Tftqm/LROuWmb8tO27sqLYXagiq+srOSiiy7ihx9+IBKJ4HK5GDx4MP/617/Izs7u8MlN0+See+7h2Wef5emnn6aiooKHHnqISy65hPHjx7Ns2TIuvvhizjrrLM4777wOH19ERKQ/i0QiLF++nMzMzE43WyQ+RKNRysvLGTVqVEx3JFYtKCIiItI/dKhhCM2F3ZIlS9i8eTM5OTmMGzeuU4Wrz+fjuuuuY+nSpTz44IOMHDlyp7d75JFHmD17Nq+88kqHzwHNhW0oFOrUffsqwzDIzs6mtLRUoz52QvlpnXLTNuWnbcpP67orN9FolIqKCtLT03t9w9But/fr3+fRaJTKykoyMjJ2+Fna7fZ27dTcVVQL9n56P26b8tM65aZtyk/blJ/WKTdtU37a1l21YLumJBcXF2/3/4yMDDIyMgAoLS0Fmtfmaa+NGzfy61//mry8PF566SXS0tIA+OCDD6ioqOCUU05puW0wGMTlcrX72P/NNE09oVqh3LRN+WmdctM25adtyk/rujo3ynPfs7PnSE/8nFUL9k3KTduUn9YpN21Tftqm/LROuWmb8rNz3ZWTdjUMDznkkJY50T8NxDAMTNPEMAyWLVvWrhPW1tZy1llnMXnyZG655ZbtPiU3TZNbb72VQYMGMXnyZL777juefPLJXrMQuIiIiEhfpFpQREREpH9pV8Pwww8/7LITvvLKKxQXF/P222/zzjvvbHfdwoULue6667jxxhspLS0lIyODSy+9lOOOO67Lzi8iItJfaGHovidWP1PVgiIiIiL9S7sahvn5+S3/rq2t5eOPP6asrIz8/HwOOuggvF5vu0949tlnc/bZZ7d6/SmnnLLdNBQRERHpnG27w4ZCIZxOZ6zDkV0QCoViutuvakERERGR/qVdDcNtFixYwEUXXYTb7SYnJ4fi4mJuvfVWHn/8cYYPH95dMYqIiEgneTwe6uvrgeYFkXuraDRKNBqNdRgxEQqFqK+vx+PxxDoU1YIiIiIi/USHGoZ//etfOeecc7jwwguB5nVm7r//fm666SaeeuqpbglQREREOs/tdgNQX1/fqxeJtlqtRCKRWIcRE4Zh4PF4Wn6WsaRaUERERKR/6FDDcO3atZx33nkt/zcMgwsvvJAnnniiq+MSERGRLuJ2u3G73b12ZznDMMjOzqa0tLRXxr8rYjkNeWdUC4qIiIj0Dx1qGA4ZMoSFCxeyzz77tFy2atUqCgsLuzwwERER6Vrx1nxqL8MwsFqtWCyWftcwjDeqBUVERET6hw41DCdNmsSFF17Iz3/+cwYNGkRZWRkvvvgiEydO5P7772+53SWXXNLlgYqIiIhIbKkWFBEREekfOtQw/P7779l9991ZtmwZy5YtA2DYsGFUVlZSWVkJ0CtHLoiIiIjI/6ZaUERERKR/6FDDUItZi4iIiPRfqgWlP9jW9O4NzW8t0yAiIt2lQw3DUCjE7Nmz2bx5M9FotOVywzC4+OKLuzw4EREREYkfqgWlr8vIzMRua/4TKTs7O8bR/G+hcJiK8vJYhyEiIn1QhxqGM2fOZP78+QwfPny7T9xUJIqIiIj0faoFpS8zDAO7zcY9by/B7nTj8/uA+B3B57RZuWL6OAzD0EhDERHpch1qGM6bN4/XX3+dAQMGdFc8IiIiIhKnVAtKfxAIRzBtUYLhKPHcMBQREelOlo7cODMzk5SUlG4KRURERETimWpBERERkf6hQyMMr7nmGi6//HJ++ctfkpSUtN11++yzT5cGJiIiIiLxRbWgiIiISP/QoYbhokWL+Oyzz/jss8+2u9wwDJYtW9algYlI9+vo7n+x3DVQa/OIiMSeakERERGR/qFDDcNnn32Whx9+mP333x+LpUOzmUUkzvx0F8COisWugdoFUEQk9lQLioiIiPQPHeoWOJ1OpkyZogJRpJf7cRfAxQTCkY7cE6/H2+O7BmoXQBGR+KBaUERERKR/6FC1d/bZZ3PnnXdSW1vbXfGISA8KhCMEw9GOfUU6ePsu+OpYU1NERLqLakERERGR/qFDIwyfeeYZiouLeeKJJ3a4TuvWiIiIiPRtqgVFRERE+ocONQxvu+227opDREREROKcakERERGR/qFDDcOJEyfu9PKqqqouCUakt4vF7sGd0VviFBGR+KJaUERERKR/6FDDcPHixdx+++2UlpYSjUYBCIVCVFVV8f3333dLgCK9xa7sOiwiItIbqBYUERER6R861N246aabKCgoYPjw4RQVFTFlyhSefPJJZs6c2V3xifQKnd91ODYSXXZ+M20MoJGGIiLSfqoFRURERPqHDu2SvGrVKm699VZOO+00IpEIZ599NnfffTdvvPFGh066fPlyzj77bCZOnMiUKVP43e9+1zKVZdGiRZx88smMHz+eQw45hBdffLFDxxaJpU7tOhyDr97Q1BQRkfijWlBERESkf+hQwzApKQmXy0VBQQGrVq0CYM8992Tz5s3tPkZTUxPnnXce48ePZ968ebz55pvU1NRw/fXXU1tby/nnn8/xxx/P119/zS233MKtt97K4sWLO/aoRERERKTLqRYUERER6R861DAcOnQozz33HE6nk4SEBJYtW8aaNWs6tIFCcXExo0aN4uKLL8bhcJCamsqMGTP4+uuvee+990hJSeG0007DZrOx7777cuyxx/LMM890+IGJiIiISNdSLSgiIiLSP3RoDcPLL7+ciy66iClTpnDuuefyi1/8AqvVyqmnntruYwwdOpRHHnlku8veffddRo8ezapVqxgxYsR21xUWFvLSSy91JEwRERER6QaqBUVERET6hw41DCdMmMAnn3yC3W5nxowZ7LbbbtTX1zNlypROndw0Te655x4+/vhjnn76aZ588kncbvd2t3G5XDQ0NHTq+NC8GUVHPvXuD7blQ3nZuc7k58fbGvSOjUS2PcYf/92uexk/fjfNnnycveM5q9dW25Sf1ik3bVN+2taTeVEt2DfoNbVzP+aFlu89W+90VM//HPXcaZvy0zblp3XKTduUn7Z1V1461DA0TROn0wnA+vXr2bJlCxMnTuzUiX0+H9dddx1Lly7l6aefZuTIkbjdburr67e7XVNTEx6Pp1PnAEhPT+/0ffu67OzsWIcQ1zqTH6/HSzAS7YZoupbHYW3+nuDF3ol4PR5vV4fUJoe1efWE3vKc7S1xxory0zrlpm3KT+ypFuxb9JraOU9Ccz3X0/VOR8WyPtJzp23KT9uUn9YpN21TfnpWuxqG9fX1XH755eTl5fGXv/yFefPmceGFF+L1ejEMg2effZYhQ4a0+6QbN27k17/+NXl5ebz00kukpaUBMGLECD777LPtbrt69WqGDx/egYe0vcrKSkKhUKfv3xcZhkF2djalpaWYphnrcOJOZ/Kz7T4+v49gOP4bhriaX/r+Bh+BUPvjNYzmZqHf76MnnzoOW3NBHO/PWb222qb8tE65aZvy0za73d7tTTHVgn2LXlM7ty0v/gYfdmdCj9c7HRWL+kjPnbYpP21Tflqn3LRN+Wlbd9WC7WoY3nvvvYTDYc466ywA7rzzTk444QRuvvlmHn/8ce69917uueeedp2wtraWs846i8mTJ3PLLbdgsfy478q0adO44447eOKJJzjttNNYsGABb7zxBg888EDHH9lWpmnqCdUK5aZtncuPufUr3jXH2Pzw2h/vtmk5Hb3frtsWb+94zvaWOGNF+WmdctM25WfneiInqgX7JuVm57alpOfrnY6KXX2k507blJ+2KT+tU27apvzsXHflpF0Nw48++oinnnqK/Px8KioqWL58ObfeeisAJ554Ig8//HC7T/jKK69QXFzM22+/zTvvvLPddQsXLuSxxx7jlltu4b777iMtLY0bbriByZMnd+AhiYiIiEhXUi0oIiIi0r+0q2FYXV1Nfn4+AIsWLcLtdjNq1CgAEhMTaWxsbPcJzz77bM4+++xWrx87dizPP/98u48nIhAIR6ltDFPbFMEfjNAYihIIRwlHTSJRE8MwsBpgsxq4bBaS3XbeWlxChT9Egt3AZtHisSIi0jrVgiIiIiL9S7sahgkJCfh8PrxeL9988w3jx49v2YWlqKiIpKSkbg1SRH5kmiZVDWFK6oKU+oJU+EL4gq2vQ2i1ACZE/muU8mfrvgWa99dLTbCR6bGTm+QgL9mB227tvgcgIiK9jmpBERERkf6lXQ3DKVOmcN9993H00UfzxhtvcPHFFwPNjYtHH32USZMmdWuQIv1dOGqyuTbAhuoARdUBmn6ysUqK20ZhhoMUt41klw2v04rbbsFps2A1ftxi3TRNwlGz+b6GwX4j8nn68zWU+4JU+EOsKG9kRXnzCJEMj43BqS4Gp7tIdnVoM3UREemDVAuKiIiI9C/t6gT89re/5ZxzzuHJJ59k8uTJnHzyyQBMnTqVQCDACy+80K1BivRHpmlS6guxuryRdVVNBLcOEUx12xie6SYvyUF2oh271fI/jtTMMAzsVgO71YLXZeP48fms2FROMBwBwBeIUFIXZHNtgKKaAN9s8vHNJh9ZXjsjMt0MTXdht2rkoYhIf6RaUERERKR/aVfDMCcnh7feeovq6mrS0tJaLr/kkkuYOnUqmZmZ3RagSH8TikRZXdHEstIGqhvDAKQl2CjMcDMo1UlSN4348zqtDM90MzzTTSRqUlIXZE1lc7Ny3roQX22sZ2RWAvsMdaIVD0VE+hfVgiIiIiL9S7s7D4ZhbFcgAi2fLovIrguGo/xQ2sD3W/wEwiZ2q8Hu2QmMzHKTlmDv0VisFoMBKU4GpDjZd1CUNZVNLN3iZ0mJn6Vb/AzPcLNHnodETVcWEek3VAuKiIiI9B/6a18kxgLhKEu3+Fm6pYFgxMTrsLDX4EQKM1ztnm7cnRw2C7tlJzAqy83m2iBLtjSvdbiyopGRmW4mDPBqkxQRERERERGRPkQNQ5EYCUdNlpT4WVLsJxQ1SXJamTTIQ2G6G4sl/ib9GobBgBQXI/PTWVVcxYJN9Swva2RNRRN75HkYk+vBGodxi4iIiIiIiEjHtGv40ty5c7s7DpF+wzRN1lY28vKicr7d5MPtsHDQsGR+vkcGIzIT4rJZ+FOGYZCX7OSY3dM4dHgKboeFbzb5ePX7Skrrg7EOT0REuoFqQREREZH+pV0Nw6uuugqAww8/vFuDEenrKvwh3lpWxcerawlGTCYNTOTEsRkUZrixGPHdKPxvhmEwOM3FiWMzmJDvpa4pzJs/VDFvXS2BcDTW4YmISBdSLSgiIiLSv7RrSrLdbueWW26huLiY+++/f6e3ueSSS7o0MJG+JBSJ8k2Rjx9KGzCAUVlu9hqQiMse+zUKd5XVYjB+gJch6S4+X1fHirJGNlYH2G9wEoPTXLEOT0REuoBqQREREZH+pV0Nwz/84Q+8+OKLmKbJ/Pnzd7je6GUjo0R60ubaAPPW1eELRMj22tlvSFKP73rcE1LcNqbvlsrqiibmb6zjw1U1jMx0M2lQYlxs3iIiIp2nWlBERESkf2lXw3D69OlMnz6dk08+maeeeqq7YxLpEwLhKF9trGdleSM2i8G+gxLZLTuhT/9RZRgGwzPd5Cc7mLumlhXljWypDzK1MIUMT99rkoqI9BeqBUVERET6lw7tkvziiy/i9/uZO3cumzdvJisri4MPPpikpKTuik+kV9pcG+CTNbU0hKLkJTnYf2gSic7+syl5gsPKkaNSWVLSwDeb6nljaSX7FCQyOqdvN0xFRPo61YIiXcM0TXzBCLWNEfzBCA2hKMFwlHDUJGqC1TCwWsBhs+C2W0iwW0h220h0WnvdutciItI7daiDsWHDBn71q18RCoXIy8ujuLiYv/3tb/z73/9m+PDh3RWjSK8RiZp8tbGOJSUN2C0GBwxNYniGu182yQzDYFyeh9wkB3PW1DB/Yz0ldUEOGpaMw6YpyiIivZFqQZHOCUdNttQF2VLf/FXlDxOKmh0+jsVoXgYm02snN8lJUVUDmsMhIiLdoUMNw1tvvZUjjzySq6++GovFQjQa5Y477uC2227j0Ucf7a4YRXqFteU+XllcQYU/RKbHztTCZJJc/WdUYWsyvXaOH5POZ+vqWFPZxOtLK5k2IpVkt3IjItLbqBYUab9w1GRjdRPrKpvYVBskvLVBaLcYZHjtpLptpLhteBwWPA4rDpsFmwUshkHUNAlHIRiO0hiK4gtGqGsKU9MYodIfYkVZIyvKGjng9o8ZkOxkypBkDh2Rwugcj0YgiohIl+jQX+yLFi3ivvvuw2JpHh1ksVi4/PLL2X///bslOJHe4s2lFdw15zsaQxH2zPMwPt+LxaJibRu71cJBw5JJ99j5emM9ry2tZOqwZAamahdlEZHeRLWgyP9W0xhm6RY/ayubCEaam4TZXjsDU13kJTtIS7C1v6nntO70Yn8wQqU/TEaKl4+XlfLCd2W88F0Z2V47hwxP5dARqezex9fOFhGR7tWhhqHVasXn85GWltZymc/nw+12d3lgIr1BUzjKXR8X8eYPleQmuzi8QJt7tMYwDMbmekhLsPHx6hreX1nDXgO87JHnUTErItJLqBYUaV1JXZDFxT421QYBSHXbGJ/pZliGC7d9542/zvI4rKQm2Lnm2HGUlJSwvLSBD1ZV8eHKGp5bWMZzC8sYkubixHGZTN8tDY+ja88vIiJ9X4cWEjv44IOZOXMma9euJRgMsmbNGq6++moOPvjg7opPJG4V1wa44D8rePOHSiYPSmL2ZQeQl+yMdVhxLz/Zyc9Gp5PqtrFgk4+5a2qJdGINHxER6XmqBUV2VFof5O1lVcxeVsXm2iCD05wcs3saJ4xNZ0yup8ubhf/NMAxGZSdwyf4DeOXs0TwyYyS/2DOTcl+Iu+YU8bNHl3Dnx0Wsr2rs1jhERKRv6dAIw5kzZ3LppZdy1FFHtYwIOuigg7jqqqu6JTiRePX5ulpufHc9vkCEcyflcO7kPFI9jliH1WskuWwcOzqNOWtqWVPZhD8Y4bARqTi1GYqISFxTLSjyo/pAhK831rOuqgkDKMxwMT7fG9M1rA3DYHSOh9E5Hi7cL4/3VlTz0qJyXl7c/LXf4CTOnpjLmFxPzGIUEZHeoUO/zVJSUnjqqacoKiqisrKS/Px8MjMzuys2kbgTNU0en7+FR+eX4HVaueu4Yew7OFlTajvBbrVw6PAU5m+o54fSBt5cWsnho1JJdGozFBGReKVaUAQiUZPFJX4WbfYRMWFgipN9BiaSEmcburntVo4bk8HPRqezuNjPC9+VMWd1DZ+vr2OfgkR+NTGH8fle1bEiIrJTnfqtVlBQQEFBQVfHIhLXmkJRbn5/PR+tqmFEpptbjx6qKci7yGIY7Ds4iUSnlfkb63ljaRXTRqSS6dU6kCIi8Uy1oPRXZfVBPl1XR01jmBSXlcmDk8iP83rQMAz2yPeyR76X9VWN/PvrUt5fUcXXRfXskefh/H3zmDAgMdZhiohInImvj8FE4lSZL8g1b6xleVkDhwxP4Q/TBuOya/psVxmT68HjtDJ3dQ2zl1Vx6PAUBqTEd/EtIiIi/UfUNPlus4/vNvsxDBif37xxm9XSu0bnDU5z86cjBnPupFye+mYLby2r5OKXVzF5UBIX7pfHyKyEWIcoIiJxIqYdj6qqKqZNm8b8+fNbLvvTn/7EmDFjGD9+fMvXCy+8EMMopb9bVurn3OdXsLysgXMn5XDz9CFqFnaDIWkupu+WhtUC762sZk2lFuYWEenrVAtKb+ALRJi9rIqFm/2kJtg4fkwGEwZ4e12z8KcGpDi57rBBvHDmaI4Ymcr8DXX86rnl/PHtdWyqCcQ6PBERiQMdGmH41ltvMW3aNByOXd/cYcGCBVx77bVs3Lhxu8uXLFnCzTffzAknnLDL5xDZVR+srObm99YDcNORg5k2Mi22AfVx2YkOjt4tjXeWVzNndS3BsMlu2fqkW0QkXqgWlP5mXVUT89bWEoyYjM5JYO+CRGy9uFH43/KTndx45BBO2yubf31ezPsrq/lodTUnjcvinEk5Md3ARUREYqtDw6T+/Oc/d8miuLNmzeKqq67iyiuv3O7yYDDIypUrGTNmzC6fQ2RXmKbJI18W84e315HksvHASSPULOwhqQl2jhmdRpLTyufr6/husw/TNGMdloiIoFpQ+o9I1OSzdbV8tKoGiwHTRqQweVBSn2oW/tTwzATuOq6QB04azsjMBF74royT/72UFxeVEY6oDhMR6Y869JHR2LFjmT17Nscdd9wunXT//ffn2GOPxWazbVcoLl++nHA4zH333ceCBQtITEzk5z//Oeeddx4WS+emgBqGoZ2//su2fCgvOxeMmFz63ELeXFzCiEw3d/6skKzEtkdS/JhLY+tXvNv2HPjx3+26l/Hjd9PsvseZ6LRzzOh03llexYJNPkJRE9M04/45q9dW25Sf1ik3bVN+2taTeVEt2DfoNbVz2/LRFI7w7tJNFNc0kZfk4KBhKSQ4rDGObmea47VYLF324epeBck8emoS7y2v4v55m/j7nE28sriCyw8sYL8hyS052pVz9uUPgvXaapvy0zrlpm3KT9u6Ky8dahjW1NRwzTXX8Ic//IGMjIztgvrwww/bfZzMzMydXl5fX8/EiRM544wz+Pvf/86yZcu4+OKLsVgsnHfeeR0JtUV6enqn7tcfZGdnxzqEuFPXFOLyf3/D/HVVHDk6h7/P2IMER/tfJl6Pl2Ak2o0Rdg3P1qLXk+DF3ol4PR5vV4e0Ay/wi328vL6omMXFfq6f9T23HD8GSy/4ZF+vrbYpP61Tbtqm/MSeasG+Ra+pHS3fUsfLiyqoawozfmAK+xdmYInTP1DddiuRqElWVlaXH/usnBxO3m8ED3+ylofmruXKV1dx4IhMbjh6N7Jgl84ZiZq9ev3H9tBrq23KT+uUm7YpPz2rQw3D008/vbviAGDKlClMmTKl5f/jxo3jrLPOYvbs2Z0uEisrKwmFQl0VYp9gGAbZ2dmUlpb26U/4OqrCH+LKWatYVdHIr/YbzAUT06mrqqCuHffdllOf30cwHP8NQ7auR+Nv8BEItT9ew2huFvr9PnrqqTOtMIWPVlfz3Fcbqa33c/20QXE7HUivrbYpP61Tbtqm/LTNbrf3WFNMtWDfoNfUzn26tpYb31lHYyjCobtlMSTZSoPfH+uwWueyYbUY3PvOEppCkW47zUl7ZDJ/Qy2frCzniJXl7FmQwvg8F05bx0ddOm1Wrpg+ts8+9/Taapvy0zrlpm3KT9u6qxbsUMPwp4tPV1VVkZbWtWu6ffDBB1RUVHDKKae0XBYMBnG5XJ0+pmmaekK1Qrn5UVF1E1e8upriuiAX7ZfH747dvZNvRubWr3jXHGPzw2t/vNumIXf0frvCZoXpu6WxtibC7GVlBMIRbjxiCDZrfDYNQa+t/0X5aZ1y0zblZ+d6MieqBfsW5aaZaZo8s6CUBz4rJtXj4LARqQzOTsbn8xHfdV1zbE2hCMFw9zUMHVY4YGgyo7LcfLmhnoVFNSwtNpgwwMtuWQkdnP2xrQbt28+9vv74dpXy0zrlpm3Kz851V046tBhMOBzm7rvvZq+99uKQQw6hqKiIn//855SXl3dJMKZpcuutt/LFF19gmiYLFy7kySefZMaMGV1yfJGdWV7awAUvrmRLfZDrDh3IWRNztTZCnLFaDB44bS8OGZ7Ch6tq+P3stb1jJKeISB+jWlD6mqhpcs8nm/jnZ8UMTXfx2sVTyEt2xjqsuJTpdXDM7ukcOToHu9Xgyw31zFpSwaaaQKxDExGRbtChhuE//vEPvvzyS+69996WIY85OTn85S9/6ZJgpk2bxnXXXceNN97I+PHjufrqq7n00kt3eWFtkdZ8tbGOi19eiT8Y4bZjhvKzMRmxDkla4bBZuGn6UI4Ymcona2u55s21NKlpKCLSo1QLSl8SikT587vr+c935eyZ7+VfJ4+kIC0h1mHFNcMwGJmTyEnjshif76U+GOHdFdW8t6KamsZwrMMTEZEu1KEpyW+88QbPPfcc2dnZGIZBQkICt956K9OmTet0ACtWrNju/6eccsp201BEusv7K6q46b0NuO0W/n5cIXvkd/9GHrJrbBaDPxw+GIfNwhtLK7nqtTXc8bOhuO3xuHOhiEjfo1pQ+orGUITr31rHlxvqOHBoMn+ePkT1RAfYrM1TkkdkuvmmqJ41lU1sqg2we3YC4/O9OG2d29VcRETiR4cahg0NDS1r1WybI+1yubBY9AtBuk93TA9+YWEZd88tItNj554ThjMsw73D+TpyXk1h7jlWi8G1hw7EbjV4ZXEFV8xazd+PK8TjVJEvItLdVAtKX1DbFOaq19bw/RY/x+yezjWHDozbDdXinddpZWphCrtlB/lyQx1LtzSwpqKRvQYkMiLLHbc7TIuIyP/WoYbhnnvuyf3338+VV17Z0iB56qmnGDt2bLcEJ5KRmYnd1qGnaZtM0+TO91bwz7lFDM308OQ5ExmQuvOpJ9qyPX5ZDIOrphbgtFl47tsyLpu1intOKCTR2XXPFRER2ZFqQentqvwhLp21irWVTZyxVzYXTcnTB79dIDvRwc9Gp7O6oolviur5bH0dy8oamDQwUWtCioj0Uh366/r3v/89Z511FrNmzcLv93PUUUfh9/t5/PHHuys+6ccMw8Bus3HP24sJdMHOb1HTZO7qWpaXNZDltXPgEC/PzFuxk1saeD1efP7274yX6LLzm2ljABWcPcUwDC7dPx+n1cITX2/h0ldWce/xw0l2q2koItJdVAtKb1bhD3HJyyvZUB3g4il5nL53TqxD6lMMw2B4ppvBaU4WFfv5vsTP28urGZTqZO+CRFJUo4mI9CodetcuKCjgrbfeYs6cOWzevJmcnBymTp2K16u136T7BMKRXd4RNxw1+XhVDRtrAgxIdnDI8BSshtHKcQ2CkejW69rXMOyKhqZ0nGEYXLBfHnarwf99WcIlr6zivhMKSU2wxzo0EZE+SbWg9FZlviCXvryKjTUBLj9wAKeMz4p1SH2W3Wph74JERma6+WpjPeurA2ysDjAi083EQUmxDk9ERNqpwx/zOJ1OcnNzsVgs5Ofnq0CUuBcIR3l/ZTWl9SGGpbs4cGgyFq1T06ecMykXm8Xgwc+LufjlVfzjxOGke9Q0FBHpDqoFpbfZUhfkkldWsbk2wMypBZy0R2asQ+oXEl02Dh2RSml9kK+L6llR3siayia83uWcOMqLV+tPi4jEtQ41DDds2MAFF1zApk2bSElJobq6mt13351//vOfZGXpUzqJP/5ghHeXV1PdGGZMTgITByZqnZo+6sx9crBbDe77dDO/eWkl//j5cLK8jliHJSLSp6gWlN6mpC7AxS+voqQuyDWHDOT4sRmxDqnfyU50cPRuaRTVBFiwyceDc9bwzBdWztwnh5P2yNSOyiIicapD784333wzkydP5ptvvmHevHnMnz+fwsJCbrrppu6KT6TTahrDvLG0kurGMPsUJDJpUJKahX3cqROymTm1gI01AX7z0iq21AVjHZKISJ+iWlB6k7L6IBe/3FwPXH+YmoWxZBgGA1NdnLxnJnedvAcJDiv3z9vMjCeX8tr3FYSjJoZhxOWXiEh/1aERhkuWLOGBBx7A4WgeteP1evnjH//I1KlTuyM2kU4r8wV5b0U1wbDJgUOTGZ7pjnVI0kNO2iMTm8Xg9o828puXV3L/icO1O5+ISBdRLSi9RZU/xKWvNI8svP6wgRw7Ws3CeGAxDH6+1wCOHpfL019u4J8fr+a2Dzfy1IJyLj64kJP2GoAjzkYcRsJBysorYx2GiEiP61DDMD8/n40bN1JYWNhy2ZYtW0hJSenquEQ6bVNNgA9X1WBiMm1kKgUpahb1N8ePzcBuNbjl/Q1c9NJK7v/5cApSXLEOS0Sk11MtKL1BbWOYy2Y1b3Dy26kD1CyMK80j9sKf3suMQD3HjLfwfHEWTxRlc/2sJfxj9jecN3ALx+dU4LC0b/PB7mTYnHinXYthGJhm7OMREelJ7WoYvvrqqwBMmDCBX//615x77rnk5+dTVlbGY489xmGHHdadMYq02+qKRj5ZW4vDanD4iDSyErWGXX919O7p2CwGN723nt+81LwRyuA0NQ1FRDpDtaD0Fr5AhCtfXc2ayiZ+MyWPk/fQ2prxyIwEIBzAA5ybt4FTs4t4viSXJzbnc/OqQTy4PpfT84o5OWcLSbZI7OKM2ZlFRGKvXQ3D++67r+XfhmHw2GOPbXf9O++8w9VXX921kYl00JISP19trMfjsHDkqDRS3B3eBFz6mCNGpWGzGvzpnXXNG6GcOJxhGZqeLiLSUaoFpTdoDEWY+dpqlpU1cPbEHM7YOyfWIUk7JVijnDNgM6fklvDilhyeKs7jng2D+b9NAzgpu5TT84rJcWptahGRntSujspHH33U3XGIdJppmnxd5GNJiZ8Ut40jRqbidVpjHZbEiUOHp2KzGNwwex0Xv7yS+04czojMhFiHJSLSq6gWlHgXCEe55o21LC7xc+r4LH49OTfWIUknJFijnJVfzC9zS3i7IoPHN+fz7+J8ninJZVp6JafmlrBnYj3ai0REpPt1eAjWN998w+bNm3dYw+H444/vqphE2i0aNZm3rpZVFU1kee0cPjIVZ5wtlCyxd9CwFG47ZijXv7WWS15exT3HF7J7jifWYYmI9EqqBSXehCMmN8xex9dF9ZwwNoNLD8jX7ra9nN1i8rOsco7NLOfT6lSeLM7j7YpM3q7IZDePj1NzS5ieUYHLGu2ReCwWS7vWMNz2vGvv7btLNNozeRGRvq1DDcM//elPvPTSS2RlZW33S9gwDBWJ0uNCkSgfra5lU02AghQnhxSmYLOqOJSdmzIkmduPHcbv3ljDpa+s4m/HDmPvgsRYhyUi0quoFpR4E4ma3PjuOuatq+XIUWlcdXCBmoV9iGHAgWnVHJhWzZoGN8+V5PJ6WRZ/XD2cu9YP5pjMck7ILmWkp6F7ArA5iESjZGV1bC3Mjt6+q4UjUcrLSmMag4j0fh1qGM6ePZsXXniBMWPGdFc8Iu3SFIry3spqyn0hhme42X9IEhaLikNp26RBSdx7wnCufn0Nv31tNTcdOZiphamxDktEpNdQLSjxJGqa/PWDDXy4qoaDC1P4/bRBWNQs7LOGJTRyw7C1XD5oA6+XZfGfLTk8U5LHMyV57O7xcWJ2KdMzy7t2kxSrA6vFwgNPv0CgqbFdd3HYHQRDsVtv0ely8ZvTT8FisWikoYjskg41DBMTExkxYkR3xSLSLr5AhHeWV1HbFGFcroe9C7z6JFnabc98Lw+cNJwrXl3N72ev45pDIvxsTEaswxIR6RVUC0q8ME2Tu+YUMXtZFfsNTuLPRw7Gpg+P+4VEW4TT8kr4ZW4Ji+oTmVWazTsVGfxl7TBuXzeEA9OqmJ5RwYGp1V02ZTnQ1Egw0J6GoQFmlGAwiPZYFpHerkMNw4suuojf//73nHvuuSQlJW13XV5eXpcGJrIz1Q0h3l1RjT8YZdLARMbkah066bjhmQn86+QRXD5rNbd+uJG6pjCnaydFEZH/SbWgxAPTNPnnZ5t5ZXEFew1I5K9HD8Vu1RrW/Y1hwJ5J9eyZVM81Q9fybkUGb5Rl8WFlOh9UZpBgiXBIeiXTMyrYN6UGu0UNPBGRjuhQwzAQCDB79mzefPPNlstM08QwDJYtW9blwYn8VGl9kPdWVBOKmkwdlsywDHesQ5JerCDFxUMnj+CKV1fzz8+KqWkKc/EULZIuItIW1YISDx6bv4VnFpQxNtfD7ccO1YZ3QoI1ygnZZZyQXUZZwMG7lem8U57Jm+VZvFmeRbItxGHplUxLr2Sf5Foc25qH0RBGqAEj1Igl3PzdCDeCGYFoFMPmgKYtHFK9lGAUQoaDEA7Chp2g4cBv8eKzJOG3JOG3eDCNDu8pKiIStzr0jvbAAw9www03sP/++2Ox6Bez9JwN1U18vKoGwzA4fEQqA1KcsQ5J+oBMr4MHTxrBzNfX8MyCMuqaIvzukIFdNqWpNzUfY7mTn4j0HqoFJdaeWVDKI/NLGJnl5u/HFZLgsMY6JIkzWc4gZ+SVcEZeCUVNTt6tyODt8nReLs3h5dIcPEaAAx3LmWZ8zaHmfJINf9sH3Pwle7TjvFEM/BYvtbYsKizpVFkzqLJmUGnLosaSBobeM0Wkd+lQwzASiXDqqad2VywiO7V0i59P19TitFk4fGQqmV57rEOSPiTJZeO+Ewq5/q11vLG0ktqmMH8+Yggu+64VdVmZ6Vhtji6KsvtFwkHKyitjHYaIxDnVghJLLy8q5/55mxma7uKe44fjdapZKDthmliaqrD6tlDoL2WkfwtXRKtZ48jjvehevB/Zm3cCY3mbPbByNvs4N3GIdwMHJ5eQnxDBtLnBYsM0LODJwHvAxfzr2ReJBuqxmyFsZhA7IZzRAB6zHk+kHm+0Dm+0nsRoDRmhzQww12wXUsBwUmrLo9SWzxZbPltsA6iyZjTPqxYRiVMdahieeOKJPPnkk5x55pndFY9IC9M0+ft7K/hkTS2JTitHjkolyaVh/tL13HYrtx87lL+8v4H3VlRz8csruePYYaR5OtecNgwDq82B7/3bMMOBLo626xk2J95p12IYhkYaikibVAtKrLz1QyV3zimiIMXJfScMJ8WtmlB+ItyErX4T1tqN2Oo2Ygn9OGow6kgknFrIAE82v3IbnOlaS7lZxZzqdOZUpfFFTQFfVg7ir5Uw1N3AganVHJhWxZ6J9dhdyZCYTaPVS9Danga1gcNuxxaoJi1cRlqknIxIKdnhYrLDxQwMrWu5ZYPhYZN9EJvsg9lkH0ypLY+opjSLSBzp0DvS4sWLefzxx7n33ntJTk7ebrrdhx9+2OXBSf8Vjprc/tFG3lhaSabXzrQRKbjt+hRZuo/dauFPRwwmN8nBv78u5dwXVvD344YxJL3za2Wa4QD0goahWoQi0l6qBSUWPlhZzV8/2EBOooN/nDic9E5+oCd9ixH0Y6tZja16DVbfFoytFU3ElUowdRiRxHwinmxM+46bJGYQ5qScUk7KKaUhYuGLmhTmVqXxaXUqTxTn80RxPonWMPtmNHB48iYawkb7/3A2DBosXhocHjYx5MfLzSipkUpywsXkhTeSH9pAYXA5I4I/ABDEQZFjCOvtw1nvGE6FNUsjEEUkpjrUMDzppJM46aSTuuzkVVVVzJgxg7/85S9MmjQJgEWLFvGXv/yF1atXk5qaykUXXcTJJ5/cZeeU+NcYinDD7HV8vr6Og0ZkMjzNigY9SU+wGAYX7pdPfrKTv320kfP/s5K/Hj2EfQYm/e87i4j0A6oFpad9uraGG99dR1qCnX+cOJzsxN6z3Id0PSPUgK16Nbbq1Vh9JRiAabERThlCJGkg4aQCTGfH6rYEa5RD06s4NL2KqAnLfF4+qU7lk+pU3itN4r0XFwHpZDkSGeRqZJC7kQx7qOO9PMNCtS2Talsmy7auimg3A+SFihgQWsfA0FoGB1czLLgC/FBvSWK9vZD1juFscBTityR28IQiIrumQw3DE044octOvGDBAq699lo2btzYclltbS3nn38+l112GTNmzODrr7/m4osvZuTIkYwbN67Lzi3xq7ohxFWvr+GH0gaO2i2de0/fm7/PXkQwHI11aNKPHDs6g+xEB9e/tZYrX1vN7w4eyM/GZMQ6LBGRmFMtKD3pq4113DB7HYlOG/84sVCb3vVXZhRr7QbsFT9gq92AgYlp2AinDiOcWkg4eRBYumbUqcWA0Yk+Rif6uGhgERW2LBZkncRDb89ng99BWdDJ13UpJFjCDHQ3McjVyABX04+7LndQyHCywVHIBkchn9HcQCwIrmNIaBWDg6sYG/iWsYFvASix5bPGsRurHaMoteVpExUR6XYdahieccYZre76+eSTT7b7OLNmzeK+++7j6quv5sorr2y5/L333iMlJYXTTjsNgH333Zdjjz2WZ555RkViP7C5NsCVr66mqCbAWftkc+F++dit+kUosTFxYBIP/2IkM19bw60fbmRFWQNXHDRAz0kR6ddUC0pPWbi5nmveWIvTZuHeEwoZnNb5JUKkdzKaarBXLMNeuRxLuAETg0hSAaH0UYSTB4O1+6emZzgjnLTXAIoWfkBjUyNbAk42NLnZ2Ohmud/Lcr8XCyZ5zqatDcQmsnZhEGzIcLLWOYq1zlEAeCO1DA6tZkhwJUOCK9m/4QP2b/iAeksiaxy7scYxig2OQkKGRt6KSNfrUMNw21SRbaqrq3nnnXeYMWNGh066//77c+yxx2Kz2bYrEletWsWIESO2u21hYSEvvfRSh47/U4ZhtFrY9lfb8hFPeVle1sBvX11FdUOYqw4u4KQ9sn4Sn7H1q2dsO61hgGm297zbcvrjv+Nb5+LtXG66Qmyes8MyEnj81FHcMHsdryypYE1lE389emirayf992vL6OHnbuf1TH7j8b0nXig3bVN+2taTeVEt2DfE+2tqUbGPma+twWKBu48fzsisHdeg6w4/5oWW7z1b73RUz9efu5qblvtbnWAL7XgD02weTVi6EGvNWgwg6kwikDOFcMZoTGfztNwe+6lYfxzVajUM8l1B8l1B9kuppS5sZUOjmw1NLoqbXGwKuPkcSLGHGehsnrqc6wxg3YVgfdYUvrfuzfeuvTHMSMu6h4XBH9iz6Sv2bPqKEDY2OgpZ7RjFGscoAjQ31+PxvS/e33tiSblpm/LTtu7KS4cahpdccskOl5144oncfvvtHTppZmbmTi/3+/243dt/euhyuWhoaOjQ8X8qPT290/ft67Kzs2MdAgBzV5Zz8UsrCUVNHjx9L44ck7Pd9V6Pl2Ck56ckezze9t/W0bwhiyfBiz0GsXbUrsbbkdx0BcfWUX2xeM7mAC9clM+tby/n0XnrOPeFlTx4+gTGD0xt9T5ZWVkAeD0eiPSChdmtzZ9K91R+4+W9Jx4pN21TfmJPtWDfEo+vqYUbq5n52iIMw+Cpcyey16C0Ho/Bk9Bce/Z0vdNRsaw/O5sbt91KJGriPeS3218R9MOi52D+w1CxAjBg1DGwz3lYhhyE02IhlhPSHQ4nmNvnOMMBGQlB9iJIKFpHUaODdQ1O1vudLPYlstiXiN2IMjAhyOCEAEMSgnhsu/ZzKnOOooxRfM7xJIfLGdr0A0ObljI4sJJhweUAVPgGwIdFZI04EvIngCX+No6Mx/eeeKHctE356Vm7vG/76NGj+f7777siFtxuN/X19dtd1tTUhMfT+U8VKysrCYV28ulVP2YYBtnZ2ZSWlmLGeDeRVxaXc9fHG/E4rNx1wnD2zIAtW7ZsF6fP7+vRNQwNo7kI8vt97d9sxdX8UvI3+AiE4r9h2Nl4O5WbLuCwNTcMY/mc/fXeaRR4TW59fwO/eOgLfntQAcePzdju05xtz9mysjKysrLw+f29YpdkbE68dH9+4+m9J94oN21Tftpmt9tj2hRTLdj7xOtralmpn0tfXkU4anL38YXkO4MtdWFP2JYXf4MPuzOhx+udDotB/bmrtaDhtmG1GDzw9PMEGvy4Ij729M9jT/883GYDTYaL7z1TWeSZQl1dOny4Fljb5Y+jvbzJyfx6xkkEw2GCwWCbtx1gDzIg2cchGQ62+E02NLnY0Ohmrd/JGr8LgGxHgGEJDQxzN+K1RXYptnKSKXfsy3zHvjiijQwJrWJYYBnDQivg0zvh0zuJuNIIDjyIpkFTCRbsj+mIbRM8Xt974oFy0zblp23dVQt2qGFYXFy83f9DoRBvvfUWubm5XRLMiBEj+Oyzz7a7bPXq1QwfPrzTxzRNU0+oVsQyN5Goyf3zNvP8wjLyk53cddwwBqW6WonH3PrVM7ZNr2gOpb3nNTtxn1jqXLydy01X2BZvbF/PR4xMY3Cqi+veWsvfPtrIVxvruPbQgSS5tn8r3Raj2cPP3c7r2fzG+ucYz5Sbtik/O9eTOVEt2LfEU25Wljdw2SurCEai3HVcIXvme2MW27bTxn9d1/P1567Wgtty62goZWL1u4xr/AoHIaotaXzqmcb3rgmEDGfzoUNtN+h6QjAY/sn/2vN4DQwD0h0h0h1BJiTV0RSxsLHJxYYmN+sb3ZTWpPJ5TQp5zm3Nwwbc1l1r+AYtLlY4x7LCORanw8kVR4zGv/BlHBvm4F45C/fKWZgWG8HcfQhsbSBGkgfv0jl3RTy998Qb5aZtys/OdVdOOtQwPOSQQ7YbTWOaJsnJyfzlL3/pkmCmTZvGHXfcwRNPPMFpp53GggULeOONN3jggQe65PgSHxqCEW58dz2frq1ljzwPtx0zjBT3Lg92FekRI7MS+PcvR3Hbhxv5aFUNy0ob+PORgxmXF9/TlkREuoJqQekOqysaueyVVQTCUW4/dhh7FyTGOiTpJslNxfDaQ5xd/CxWopRac5mfMJXlzjGYRvxNne0KLmuUEZ4GRngaCEUNNja5WN3gaV77MOBiXnUq+c4mhnuam4f2Tu64vI1pWGDgJHzOwUQnXomlvhjXxrk4N87BufkLnJu/IOmL2wgnD6Zp0FQCA6cSzNmrRzaREZHepUNdmg8//HC7/1utVtLT07Hbu+bNJTU1lccee4xbbrmF++67j7S0NG644QYmT57cJceX2Cv3Bbnq9TWsLG/kiJGpXH/YoJbppiK9RaLTxl+mD+G1gkrunlvEb15ayXmTczlzn64ZYSMiEq9UC0pXW1fZ3CxsCEW57eihTBqUFOuQpBskB7dwQOnTjKt+F8wIJc5CPncdwDr7iB93QukH7BaTYQmNDEtoJBg1WN/oZnVDAkVNbjYF3HxqpFKY0MAoj48cR7BLUhNNzKNh9Kk0jD4VI9SAY/OXODfOxbVxDt7FT+Bd/ARRh5fAgAMIDDqIQMGBRN09v3aoiMSfDjUM8/PzuzyAFStWbPf/sWPH8vzzz3f5eST2VpY3cNXrayj3hThvUi7nTMrRLkfSaxmGwfFjMxiX5+EPb6/joS9K+Hx9HXefmojGRYhIX6VaULrS+qomLnllFXWBMLcePZT9hiTHOiTpYsnBLexf+gx7VL2DlQibEvdgwIl/4cWPNxBs9MU6vJhyWMyWkYeBqMHqBg/L/R6W+70s93tJtoXYzeNjhMePZxenLG9j2hMIDD6EwOBDqDNNbJXLcW2cg3PDHFxr38G99m1MDEJZYwkMmEJgwBRCWXv02OjD3vS3oabFSn/Qrobhf08/+W+GYfDBBx90WVDS98xbW8sf31lHOGpy4xGDOWKUPrWSvmFouptHTxnFvz4r5j/flXHUfZ/y68m5XDl95zuAioj0RqoFpautqWjkslmrqG0M85ejhnDA0JRYhyRdKClYyv6lz7Bn9TtYzTAbPWOZm30WFRkTuXLIHvDxU7EOMa44LSajvT5Ge31UBu0s93tY2eDhy9pU5temMNDVxGhvPQNdTV03INMwCGfshi9jN3wTLsLSWIlz4ydbpy5/iaNsMYnfPkjUnkAwb3JzA7FgCpGkQd0yKjQrMx2rzdHlx+0ukXCQsvLKWIch0q3a1TC89NJLd3r5d999xwsvvMDuu+/epUFJ32GaJk99U8q/Pi8myWXl7uMK2SNfa71J3+KyWbjioAEcOiKV2z7exD/nbeaTdT5uzHEzwtULdkkWEfkfVAtKV1pR1sDls1bhD0a55eihHDQsJdYhSRdJCFWzf9kz7FX5BjYzRFHCaObm/Ip13glgGHh70QiyWEl3hJjiqGFySg3rG90s93tbNk1JtoUY7fUxyuPDuYtrHf63qDudxpEn0DjyBIhGsFcsxbnpMxxFn+Es+gTXho8ACCfmE8zfj0DBFAJ5kzBdqbt8bsMwsNoc+N6/DTMc/7WzYXPinXYthmFopKH0ae1qGJ5wwgk7XPbYY4/x8ssvc+qpp3Ldddd1eWDS+zWFotzywQY+WFnN0HQXfztmGANSnLEOS6TbjMvzMvuyA/jr69/x9DdbmFG8G6fnlnBBQRFeWyTW4YmIdJpqQekqP2zxc8WrqwmEo/ztGE1D7iscET+Ty19kcvmLOKONFLtH8nHuOaz17t2v1ijsSlaDlvUO68NWlvq8LPN7+bwmla9qkxmR4GdsYj1p9vD/PlhHWayEssYRyhoHEy7CCPpwFH+Fc9M8nJs+I2H5iyQsfxGAUNpIgnmTCORPIpi7N6az869pMxyAXtAwVItQ+osOb01bV1fHNddcwzfffMMdd9zB9OnTuyMu6eW21AW55s3mzU0OGpbMHw8fTIKjb+58JvJTLruVi/cfwEmThnHNE+/xRHE+r5dncsWgDRyXVYZFNbOI9HKqBaWzFhf7uPK11USiJnccO4yJ2uCk17NGg+xV+Qb7lz6NJ1JLhbOA13POZXnyAWoUdqFEW4TJKbXsnVTH6sYEltQn8oO/+Svf2cTYxHoGuxq7LeWmw9uy9iGAtW4Tjk2f4Sz5CkfxV3i+fxLP909i0jzNOZA3kWDeJII5e2M6tbq3SG/VoYbhd999x5VXXklqaiqvvPIKBQUF3RWX9GILN9fz+7fWUd0Y5rzJuZw9MQeLCgbpZ8YNSOG5Cct4rTiZezcM4o+rh/N8SS7XDV3Lnkn1sQ5PRKRTVAtKZ31TVM/v3lgDwN+PK2TCADURejPDjDC2+n0O2vI4KaEy6uwZvJE7k0VpR2IaGiTQXWwWk1EePyMT/JQGHSzxJbK2IYHNARepthB7Jtaxu6NrNkhpSyRpAI27z6Bx9xlgmlhr1uEsno+jeD6O4q/wLn4CFj+BaVgIZeze3DzMm0Qod69uj01Euk67G4aPPPII9957LzNmzOB3v/sdDkfvWZBUtrdt0fLu2IXqlcXl3DVnI06rhb8dM4yDClM6fazetEuWyM5YDTgxu4xp6ZU8VFTAMyW5nLFkHAemVvGbgRsZ7fXHOkQRkXZTLSidYRgGH62q5k/vrMNptfD34wsZlxef61mr9mwH02Ro5Vz2XX8/WU3rabQm8kHu+XydcQJhi5Ye6imGATnOIDnOSvwpNSyp97LUl8jH1el8VRchae4aDhviIsFu6ZFgIqlDaUgdSsPoU8E0sVWvamkeOou/wlH+PSx6tLmZnLcn3szxBHMnEsyZgOmIz/cDEWlnw/DCCy9k7ty5nH766Rx++OEsWrRoh9vss88+XR6cdL2MzEzstuYfe3Z2dpcdNxCO8Oc3fuDZ+RsZmJbA/525NyNz9MmxCDRPI7lqyHp+nl3K/RsH8l5lBp9UpzE1rZLfFBSxmxqHIhLnVAtKZ2RkZvLigmJumL2WdK+Tf589kd3zNA25tyrwLebQkv+joGEpIcPJvKzT+DxrBgGrGj6x5LE2T1cen1THDz4vS3xJ3Pr2cu5zWDlhbAYzxmeR4bH3XECGQThtBOG0ETSMOQPMKLaqlTg2z8dZ8jWuLd/g3bwAvnsE07ASyhxNMHcfGH1kr1i/UKQ/aVfDcM6cOQA89dRTPPXUUztcbxgGy5Yt69LApOsZhoHdZuOet5dgd7rx+X10xZKtdU1h3l9RTZkvxIBkB4cWJvHq16t3+biJLju/mTYG0Ke90jcMSWjkrlErWOEv4l9FBXxQmcGcqnSmplVyZl4xeyfVabkfEYlLqgWlMx76ZB13vreSJJeVw0ck88aCNbyxINZRtW5b7anfxdvLbFzHIVv+jxF1XxLByqKcn/NR2qn47GmxDk1+wmkxGZ9Uz14ZYQaNP5QHPlrJ0wtKeeG7Mo7dPZ0z9s4hJykGI8MNC+H0UYTTR9G4x6/Iycqi4odPsBfPbx59WPI1jrLFsOhRvBhEEzKJJOYTTswj4s0Dq0azi8RKuxqGy5cv7+44pAcFwhFMW5RgOMquNgw3Vjcxd00twYjJHnkeJgzwYjHYeuxdj1OkLxrpaeDuUStY7tvEg0UFfFSVzpyqdEYk+Dktr5ijMipwWbt//RkRkfZSLSgdETVN/vHpZp5fWEa6x8bhI1Jx2SxdUh92J9We20sKlnLQlifYo/o9DEyWJk9lTu65BNNH4fN1zcAD6Xo2A36xdwEHDLDz6Zpq/v31Fl5ZUsHrSys5evd0ztonm9ykGE4ft1gIZ4wilD6ShrFnghnFXr2KjPrlhOf/H7a6IqwNZThKF2K2NBDzCCfmE/HmglVT30V6Sod3SRaB5kLw200+FhX7cVgNpo1IYWCqK9ZhifQqo7x+7t1tORsaXTxbksurpdn8afVw7l4/mBOySzk2s5zhnoZYhykiItJuTeEoN727no9X1zBxcBpjsh2aK9LLuMO1TCl7ln0qXsVmhljnHc+HuedTkjASMNAE5N7BYhgcMDSF/Yck89XGeh6dX8Jr31fw5g8VTB+Vzln75DAgJQ6ab1tHIDJ6Kk3VpRBqwtJYidW3GWt9Mdb6YhwNZThKv9u+gejNJ5KoBqJId1LDUDqsIRhhzupaSuqDZHjsHDI8mUSnnkoinTXI3cR1Q9dx6cCNvFqWxbMluTy+eQCPbx7AKI+PYzLLOTqznAxHKNahioiItKrKH+J3b65h6ZYGDh2eyj/PnMi97yyO+5GF0swWbWJS+SvsV/YcrqifEnchH+X+mrXevdE87d7LMAwmDUpi4sBEFmzy8ej8Et78oZK3l1VyxKg0ztonJ74GfhgG0YQMogkZhLL2ANPE0lSFtX7z1q//biBmNE9h9m4dgWhTA1Gkq6jLIx1SUhfk49U1NIai7JadwKSBiVgtKiBEuoLXFuH0vBJ+mVvCt3VJvFmeyXsVGdy5fgh/Xz+YfZJrOSitiqmp1RS4m2IdroiISIt1lY3MfH0NJXVBztw7mwun5OOyW2MdlrSDYUbYs+ptDtrybxLDlVQ5cpmdcwVLUw4Gowd22ZUeYRgGexcksndBIt9uquex+VuYvayKd5ZXMW1EKr+amMPgNHesw9yRYRB1pxN1pxPKGvdfDcRirL7NOEq/224EYjhpIJGkAiLebDD0PiTSWWoYSrtETZNFxX4WbvJhtRhMLUxmWHoc/kKRPs2I80+3t8XXEqetc4s0W4C90wPsnb6J66KbmVOZwpulaXxRncz82hRuXwfDEhqZml7Dfql1jEvy4bLuwjpCW+Ps7vzukJ9dYJpaN0lEJF58tbGO699aS1M4yvWHDeTY0Rlx/zu7L2lOdSfybZqMqv2Ug7c8QkagCL8thbfzL+PbtGOIWna+q+62H6thgGl2/Jx6WsSHCQMSmTAgkUWbfTz+VQnvrqjmvRXVTBuZytkTcxmcFkcjDv9bmw3EzdjqN+Pc8g1s+QbTYm8efZhUQDipANOZoiehSAeoYSj/U30gzNw1tZTWh0h12zhkeAopbj11pOdYLQaRqEl2dnasQ2mXrKwswtEoidOu2+VjJQInbf1qCIaZt6qCD5eV8eHyUh4tcvNoUS4Oq4U9C1KYPDSNSUPTGTcgmUTXzgv91oSj0R7Lb1ecJxQOU1Fe3gXRiIhIZ5mmyUuLyrn30024bVbuPq6QfQYmxTqsfseT4MUe6di07/zaBRyw/n5yfd8TtCTwRcH5LMj7JSGbh4T2nNPTuZUMExwa7RVP9sj3cs8Jw/m+xM+j80t4b0U17/eWxuE2OzQQo1j8Zdjqi7DWFWGt3YCtdj0AUYeXcGIBka0NRGy94PGJxJC6PtKmNRWNfLa+jlDEZHROAnsXJGLTFGTpYVaLgdVicM/bSwiEw7EOpw0GXo8XSyTARdNG88DTzxNo8HX5WXKAX2ZCaZKdTU0ONjU6WLghzFfrq+Cj1YBJqj1CtjNIljNEljNEhj2Ms5VRiN7EJH596i+4950lNIW6M7/N+fH5d21nRafNyhXTx2EYhkYaiojESFMoyt8+2sg7y6soSHFy+7FD43M6Y5/WXJMHN3xFsMnfrnvkhIo4wPcOQ4MriGDlG/f+fOE9lIZgIqz/pl3HcNgdBEPBTkXsSEwGhnXqvtJ9xuR6uPv4wt7dONzGsBD15hD05kDuPhAJNo88rCvCVleEo3IZVC7DBKIJWYSTBhJOHkTUk6Up+CL/RQ1D2algOMrn6+tYU9mE227hkMKU+NhFS/q1QDgS5wunGwQjUYxIBIBAMEQw1ImNSswobrMBT9SHJ+rDaTY1f0WbcJhNOMwgViJYzEjzd0eEqB1WRXP5PlzA6kg2qyPZLPelsPwn/cpkSyNZtgbSbEGS7RFcdgteO1i3bqbS/fltzk/zOdToExHprYprA1z71lpWlTcyZUgSfzpisDbAiyUzAtG2P/DLDJewv/99RgR/wMTgB+cefOo5nBprevMN/sf9f2RsPV+ETv0uN+O5jpM+1TjcxuogkjKESMoQAoARqMNWV4S1vghb3SacW77BueUbTKuTcPJAwkmDiCQPxLTpAxAR/WaXHZTWB5mzphZfIEJBipMDhibjtuvTFpGuYjEjpEQqSYlUkRKtIjlSTXKkiqRoDd5oPZ6oDwsdL6j3oHnqMtbmrxrTw/fRIXxvDmZldACrzXxWBfNZFUzfPp6SKP/5w/OkWfyk2MMkOq04XQlY3MngTiXBacdu1XuAiIjA/A11/PGdddQ3RThvci5nT8zBojXB4lZ6uIwpDR+wW2AxACsco5nnmUaFLSfGkUk865ONw61MZxKhzNGEMkf/OH25dgO2ug3Yq1Zhr1rVPPrQk004eRDhpEFEEzK19qH0S2oYSotI1OS7zT4WFfuxGLDf4CRGZbm1aLVIJxlmlLRwGdnhYjLDW0iPlJEeKSclUon1vxqCJgb1liTqLcmU2AbgtyTit3hpsHhpMtwEDBcBi4ug4SRoOIhiI2JYiWAhatgwAYtpYjRfgsWMYiOMwwzgNYNMNgMcaC7HEfmWxrBBZcRJddhJZcRDhellSySVtaEMfIEE2G4WdTUACUaAFGsQrx1cTgc2lxeny02C3YLHYSHBYcVtt+iPRhGRPiocNXl0fgn//moLHoeVO342jClDkmMdlrQiJVLJFP8H7B74Dgsmqx2jmJcwjVJ7fqxDk15kW+NwSYmPR+dv6VONQ2C76cvB/EkYIT/W2o1bG4hFOP2lOIu/ImpzE0ke1NxATCwAm2beSf+ghqEAUO4L8enaWqobw6Ql2Di4UBubiHSEYUbIbNrI4IaNZNUvhUc38ZtN3+Iwf1zjJ4qFamsaaxyjqLJmUm1Np9aaRq01lTpLMlFjF19znejVpQODvElcfOYvuW/WHMz6UsINNYSbfDQFAjQEw/hCUB1xURpJpTiciq/RCYS2fv2UidtuJcHe3ED0OCwk2K0kbG0oJtitWBxhTNPUh7QiIr1ISV2AP76znu9L/IzMcnPzkUMoSO3ljYI+KilSzX4NHzG2aQEWoqy3F/Kp53CK7QNjHZr0MMMwumzgx7i8RO49IZElxb7/GnGYxjmT2t843BbPf8cVLwNUTLuHcMZuhDN2AzOC1bdl66YpG7BXLsdeuRwTg0hiHngTsaXtRSh1uEYfSp+ljlA/F46aLNzkY0mJH8OACQO8jMv1YNXGJiJtcoXrGdCwlAL/Ugr8S8hrWIHdDPzkBslscQymxJJDqS2Pclsu1dZ0IrvaFOwuhkGTI5VAUhJs3eDSAni3fuVFQxwQLCY9sBFPQzE0VhFprCMQaKLCTKLUTKXUTGVLJJ2SSAYlDYmE2dlOiBVYDHBvayraLXidVhKd1u2+awq0iEh8+GBlNX/7cCO+YIRTJ2Rx4b55OGx6j443SZEqJjfMZVzTN1iJUGQfzKcJh1PkGBrr0KSnbW1eZWVldfmhc3Jg2oRCFmyo5t4PV/HeinLeX1nFz/bI49JDhlOY1b7ds7Ozs7s8ti5nWIkk5hNJzCc4YD+MYD222g3NDcT6TfDhn8kAIt5cmgoOJDDwIIL5kzHt7dlnXKR3iNO/XKUnlNYH+XRtDbVNETI8Ng4Ymkxagj3WYYnEpcRgOYN9CyloWEqB/3uymta1XBe0uCjyjKYkYRTVqeMocw/hnOOO4OXHnybY2PW7JMdC1GKnwjWICtcg+MkMNMOMkBHYyB6Na8huXEN24zxyGlfjDtdRjbe5iWimscY2nLW2oRTbBlAcTqY64sIfiFDu2/mmMC7b9o3EJKeVJJeVZLcNu1UfaIiIdDd/IMI9n2zizR8qSXXb+Ptxw9h3sKYgx52KVRxe+RS7+b/GQpRiWwGfeg5nvb1Qo576q607/fo/uptooK5bTjEC+GcufJfg4V8b8njtO3j9u81Mz6rigkElDE1o2nloGHg9Hnx+P+ZPNs0xnIl4D/ktBkbcbotnOhIJZY4hlDkGLFYSR07Fv+hVnBvm4ln2Ap5lL2BaHQTyJhEYeBCBgQcRSSqIddgiuyQuG4azZ8/mqquuwun8cW2Aww47jDvuuCOGUfUdoUiUL1eVs3BjDRYD9i7wMjbXo7XHRH7CEfEz2PcdQ+oXMNS3gIxAUct1dfYMlqYcTFHCaIo8Yyh1D8M0rICB1+vFiDT1myLdNKyUu4ZQ7hrC96mHbb3QxBuuJKdxNdmNa8htXMleDXNIDr3YMos5ZDgoTSxkg3s3ltnHsMoylM1mGr5AlPpABF8gQn0gQoV/x4ai3WIwb8On5HqsDEx1UpDiYlCqi4JUJx7HzkY1ikhvo1owtr7cUMdtH2yg1Bdi0sBE/nD4YNI9+lA5nmQ1rmFq0XMw/2NGY7LBPpQvEg5hg31Yv6lBpG1mJADhwP++4S7Y0xPgX7tX8V1dIv8qKmB2WTpvl6UxPaOCCwqKGJrQ+F/3MCBi3xrXj61B0+bo1ji7nMUGhYdS7x1N3X7XY61Zh2vjXJwb5+Lc/CWuok/hs78QThlK09bmYTBnL7DqfVR6l7hsGC5ZsoTjjjuOW2+9Ndah9Dmfrirnxe/KqG2KkOm1c+DQZK1VKAJYzDD5/mUM9S1gSP0C8huWtexUXGvP5LvUI1mXOIEizxhq7dkqxttiGPjsGay2Z7A6aXLLxZ5QNcPM9aRVfkdu43LyG5YzoOEHpvAyAE0WD8UJIynxjGRz5iiK3aOosWbhC0apC0SobQpT2ximPhCh0hfk+807fnqd5bUzLN3N0AwXQ9PdDEt3MyjNhUvT50R6FdWCsVEfCHPvJ5t564dKEuwWrj64gOPHZuhD5TiS17CM/UufZWTdZ80XFB7GC3VjWW/2gime0mftmVTPv0b/wHd1iTxYVMDsikzershoo3HYt0RShuBPGYJ/3K8wgj4cm79oaSB6Fz+Od/HjRB1eAvn7bR19eGDzzssicS4uO0VLlixh+vTpsQ6jT6nwh7jvk028v7Iam8XggOEZFKba0FKF0m+ZJhmBDQytX8AQ3wIG+RbhjDYXM00WD6uS9mVt4gTWefei0lmgBmEX8NvTWOsdyGLHBMAE0yQ1WExewwryGpaT17iCAv9Shvq+bblPvS2NkoQRFLtHNX9PH0nYncE1x45nXdFmNlY3sbE6QFFNExuqmlhX1cSCTfV8seHHKTgWA/KTnc2NxHQXQzPcjMhwk5/i1B/BInFKtWDP+3RtDbd/VESFv3lU4bWHDiInqZeN+umrzCjD6+ezb9kLDPIvBmB58v4sKPgVp510MsWPPQV9ZAkU6d32TKrnoZ00Dg9Lr+Sc/M2MTfTHOsRuZzq8BIZMIzBkGpgmtsplzc3DDXNwrXsf97r3AAhmjmmZuhzKHAOGZslI/Im7hmE0GmXp0qW43W4eeeQRIpEIBx10EFdddRXJyR1fN6Urd4fqjSJRk5cXl/PQ55vxB6NM2z2bnARIS07C7/dhxusiEcC2LV+bf3w99zPc9nQxDDDN9p43NrF2Xufi7VxuusKPO6rtyuvZG6pkSH3zCMLBvm9JClUAEMHKZs9o1ibuxbrEvShOGLV1ivFPz96xOP/7st6g+98v/ys/hkGNawA1rgH8kHZo80VmhMym9c0NxIbl5DasYFjd14yo+7LlKHWOLPBPJDN5JCmZY9ht8GhMV37L9eGoyeaaAGsqG1lb0ciaykbWVDTyydoa5qz5MZoEh4XhGQmMzEpgRFYCIzMTGJLu6pYNV8z/8Wbb2q6BsRSPsVgslp3m8n/lt6+Lp59VV1At2Lquehw/fU1tqm7i7rkb+XRtLV6nld9PG8yxo9N3+Vw/3t+g9/wuNLb73vP1zvas0SBjq99n3/L/kBHYSMSwsSj1cL7MnEGZeyiJ280SikWcu3rO3vK82Kaj8cb48VkdGLbY7GY+Pi3Ew2lrWVjr4eGNubxfmcH7lRlMSqnj4uE1jE8IbZ8d69blJ4zmdQ7jXzvrNsMgkjkaf+Zo/Hv9BqOxCmfRJzg3foJz46c4FvyTxAX/JOJOJzjwQBh3HJbEsUQd7ds8pj+Jx1o5nnRXXuKuYVhVVcXuu+/OEUccwX333Ud1dTXXXHMNV199NQ8//HCHj5eent4NUfYOX6+v4sbXl7K0uI78FDf3njKaw3bP5p8frSYYieLxxPcb0ba1yDwJXuyRaM+fvwP5iXWsHbWr8fb0c+fHeD0ditceaWBA7bcMrPmKgbXzyWhY23JdRcJQvs2YxsaUiWxKnkDI+uOOZru6t5nb1XwEh90BkZ1v6hFP7I7mESQJbg82R/c/fz0eT5vX+717sIo9WLX1/9ZogAz/arJ9P5DtW0aufxnmitl4zTd/vFPqYMibAHnjIW88+UP2YOJug7Y7blMowppyH8tL6vmhpI6lxbUsLa5jUfGPozLsVoMR2YmMzktidF4yo/OSGJWbhNe5a78uI1GzXbvPx8uuge2Nt6e1tuNjvMYrnaNacOe6+nneFIrw9KIa/jV3DcFwlJ/tkcf1R+1GTnLXNhi8Hi/BXlAbwfb1RixrZVeohnFbXmbPkv/gCVXRZPXydf6ZLMydgd/Z/D7oBRK2xhuLesPh6NzoU7u9eQ23XlMjdTLezuanK7idLiJRE+8hv41ZDNscsPVr+ZY6Hpq7ltcXGZz5dRKj88ZwwUHDOGpMDratH9RGoiZeTxKEe8H0ZWvzz7fjdVsODNkduBAiYdj0Fax8F+uq93CvmAUrZpFlsUHBZBhxOAw/AjJHaqbTT8RLrdxfxF3DMCMjg2eeeabl/263m6uvvppf/OIX+Hw+vN6O/eKurKwkFIr/X0ZdaUtdkH/O+3H68Rl7Z3POpFwStv7e8jf4sDsT4n+Eoav56elv8BEI9VyhaRjNDbEO5SdGsXZaJ+PtVG66gLH1E/TQhq8JNrU+5cYwI+SGihgcXMXg4EryQhuwbl2HsN6SxBLXXmxwjGC9Yzh+axJEgaoIVH3dZbE67A7C7gRgGMFQkGAw2GXH7i6OcASA0Mavu31XZ4fdQTDU8ZwUAUVkgZFFYs6x/GbGMfzntddIq/2BnK0jEdOWzsJY+krLfWocuZS5h1GeMKz5u3sY1c48MCw4gPG5bvbMcbVsrlLhC1HhD7K+wsfS4jpgU8uxkl1WMjwOMrx2Mj12Mrx23Pb2TR1x2qxcMX0spaWlrY6EMwyD7OzsNm/TU7bFcs/bSwhsfW7EmmE0f8Dhb9jxvac9+e3r7HZ7n2mKgWrBnenK16VpmqyrauLL9fXUNoVITbBx5Kg08r0G//5keRdFDIkuO7+ZNrr31Ebwk/rIj93p7vF6J6txDXtXvMq46vexmwFq7Nm8m3cx36UdRdCa0LxxWOjH39Pb6qOerjccDkenz+fY+lrsNTVSJ+Ldlfx0BYcrgtVi8MDTzxNoiJ8pwAOBM/MtLPIlsbgkymXPLeQPtjB7JjewZ5bBlWedgq+pCdqo9eOGzYkXdr32cA6FsRfB2Iuw+krIrP6WwPdv4Nj8BcaGefD+Hwkn5hMYOLV545T8SRCjUaOxFk+1cjzqrlow7hqGy5cv580332TmzJktwyqDwSAWi6VTn9SYptlvnlBNoSjPLCjlqQVbCIRNDhiazKX751OQ2vymsi0P29LR/D2ec/PTeHsuzm1TTzp23tjE2nmdi7dzudl1Pz5nwxANb3dFRqSMQcHVDAytYVBoDU6zeTe4gOFgnWME6+2FrHcMp9Katf2ncz89TpcxwIxgRn/6x1xveD40M6PhbsrLNs35IRphV/JiRsPg8LDevTsrbCNh6+9GR8RPbuMqchtWkNu4iqymtRTWfs6I2nkt9w1aXJS5hlDqGkaZeyhlriE4nAW4klIZkOwAmkc/NoYiVPrDVDaEWr6vqWye3rxNgt1CusdOhsdGWoKddI8dr8OykykB215v//v3UTz9zgqEwwTD8fJHvoE9Et3adPjv/LQ/v31VX3vcqgVbt6uvy5K6IN8U1VPmC+GwWpg0MJHdsxOwWAyCXfwBQSAcm5ph15jbfe+J2C1mmJG189in4tWW9QmL3SP5MvMkfkiZ+pMlUna2HMN2/+vWOH/0099xu3rO3vK82KY98XZlfnZNIBjq1Ie03cmFwQFptYxzh/ne5+F7XyJzK5P4ojpK/Rs/cLJpZ4DRG54XXV97RLy5UHgu1QVHY4YacRZ/1bzr8sa5eJY+g2fpM5g2F4G8SQQGTqVp4EFEE/O65Ny9SV/6nd6VuisncdcwTElJ4ZlnniE5OZmzzz6bsrIy7rjjDk444YSYDu2OZ5GoydvLqnj4y2LKfSEGp7m44sABTBqUFOvQRLpccqSKQcHVDAqtYWBwDV6z+VPIKBZKbANY7xjOOsdwSmwDiBpx9xYn3Sho9bDBuycbvHu2XGaNBsls2kBW01qyG9e0fB/QsGy7+zZavVQ4B1LpHEiFa2Dzv90DqU7ObfljLRiJUtUQptIfonLr9021AYpqAi3HcdoM0hPspHtsW783j0YUkfZTLdj1qhpCfFPko6gmgGHAqKwE9h+RjRlqItZNjf7KE6piQtVbTKh4naRwJWHDzuLUaXydcTzFCbvFOjyRbuW2RtknuY49E+tZ1ZDA9/5kHvtsHY8zkoPSqjg9t4SJybX9dyauzUVg4IEEBh4I5g3Yatbi3Dinee3DTZ/h2jiXZCCUOrxl45Rgzniw6G8f6Vpx94zKycnhoYce4u9//zsPPvggTqeTo48+mquvvjrWocUd0zT5fH0dD3y2mbWVTaS4bfz2oAGcMDYTm7W/vrtKX5MYKmeE7wd47WHO2Tyb5Ehly3Wl1lyWOfZkvb2QTfbBBC39c4i+tC5icbAlYThbEob/eKFp4g1Xkd24hozABjKaNpIe2EhGoIiChh+2u3/YsFPtyKPKmU+1I49qZy7VCXlUp+ZRa88miJ3qxu2biGW+EMV1P36ib7MYfL3pM4ak2BmR6WZEZgJD0104bF2/uYpIX6BasOtUN4RZVOxjTWUTAEPSXOxd4CXJZcfjtOHr3TO1ex8zymDfd4yveovdaj/FaoaptWfyUc65LEw7igZ7aqwjFOlRdovJ7l4/e2QYTD5oOv/36vvMqUhjTlU6hQl+fplbwlEZFXhs8bFESkwYBuHUYYRTh+Hf41yMQD3OzZ/h3DAXZ9EneBc9gnfRI0QdSQQK9idQcACBggOIJmTEOnLpA+KuYQgwceJEnn/++ViHEdeWbvHzwGeb+XaTD6fN4Kx9sjl9rxy8Tm3HLr2YGSUjsJEC//cM9C+hwP89qcGSlqujtkwWuiaxwTGMjfahNFrie+MeiVOGgc+ejs+ezhombneVO1xLeqCIjKaNZAQ2kN5UREZgI8PrvsTC9tMATQxq7ZnUOPKoduZR486hNimLans2G6KZbAgmUdHYPCpxbYWfbzf++Je51QJD09zNDcSsBIZnJrBvYlqPPHyR3kC14K6p8IdYtNnH+urmEdB5SQ72GZhIhkcjnmMhMVTOHlXvsmfV2y11zTrvnnyTfjwrkqf8ZNqxSP9kGLBfYQbj9tjAxuogz5bk8mpZFjetKeTOdUOYnlnOydml7O719d9Rh1uZzkSahh5J09AjwYxiL1/aMnXZvWY27jWzAQhl7E5TwYEECg4glL2HRh9Kp+hZ08ssK/XzyJclfL6+DosBx45O57zJuWR5NUUnHjT/AusNv8XiI0ZrNEhu48qWBuEA/1ISInUt11c4C1iYdhRlKXtwxM9O44lX5nT7phzSvzXaktlkS2aTZ8x2lxtmhORgGanBYlKCJaQFikkJFpMaLCancRWD/d/tcCwTA58tlXpHNrlDhrPazGNpZBA/BDNZ7vewvCbIW8saeWtZ1dZ7rCQtwcbQdPfWL1fLv3vyw6Ad12AUkd7ANE221IdYXOxjU23zKOeCFCd75nnISlSd2NMsZpjhdV+yZ+VsCuu/wkKUelsan2Wdyndp06lyDoh1iCJxqcDdxDVD13HJwI3MrsjgpS05vFza/DXK4+Ok7FKOyiwnMQ5GHXZlzbTtWB06pmElnD2OcPY4/PtciqWhAsemeTg3foqzaB6JC/9F4sJ/NY8+HLAfgYIDCA48kKgnq8vi7k5aqzD21DDsJVaVN/DIlyV8srYWgEOHp3DupFyGpLtjHJn8lCfBiz0SL5sEtM7j6PlPsg0zQkZgI7kNK8hrWEFe4wqyG9dgM5tHXUWwUpIwgkWeIyjyjKXIM4YGWwoAXpedI1IKejxmkW1Mw0qNM5caZ+5OrjRxRepJDZaQFCojOVj2k+/lJIfKMJYvZzgmw4Hjf3LXSncaS13jWWEdyUoKWBlM54fi5k0Jfio32cWI7ERG5iQyPMvL8OxEhmR4SHZrtJBIfxeOmqytaGRpaQNVDc0bVw1Jc7FHnod0jSjsWaZJTuMqxla/z5iaj/CGq4liYXXSRBamHc3qpElaX1mknTy2CCfnlHJyTik/+Dy8XJrNW+WZ/GXtMO5aP5gjMir4WVYZeyU1D6TpURYrRMNkZ2d3+aF37Zg5MHQMcGHzJoObv4XV72NZ9T7ute/iXvvO1puNhcLDoHAaFEwEa3z+roiEg5SVV/7vG0q30W+sOLd0i58nv9nCJ2uaG4VTh6Vw7uRcCjPUKIwvW3dx3PAVwSZ/jGP53xzeRGBYtw3pt0RDZAQ2kt20luzG1eQ1rCCncRXO6I87zDZYk1jvHU+RZzRFnrFsThhFWGsQSm9kGDTZkiixJVHCyB2udtisXHPUaPz/z959h0dVpn0c/57pk0x6QkIg9KYCGjqCiiK6Fqwg1lV3rWBdu667NsSyNizvrusqi7rq6tp77wqIdOmdBNJ7mXrePxJGI0lMIMmk/D7XNVcyZ86cc899JifP3POc5/ngToyynRi+Miy+MgxvGXG+Mib4lnNI9TcYgZoxxkI2gyxrMuvMnqw1e7LW7MX6sl58V5LGF+vqNugSbV56u6vpHeWjd5SXPu5qekdVk+Hy4rLu3beyhjMGzxF/or30RBaR+pV7g6zJrWRNbiXegIndYrB/ahT7p0YR51YTvy3F+XYxrOhjhhV9TLJ3GwCFjnQ+SzuZZYlHU2ZPiXCEIh3b/p4K9vds4po+W3g/P5n/7Urj9dxUXs9NJc3h5diUPI5PyWNgdGXbBGSxgsVGxUf3Eqptv+0rAwNPdDTlFRWYLT0ZVcYkjF5H4EkfTOCrh7Hkb8CyawV8/RCm1UEgtjfB+D4E4vtgOmJadt97ybA58Uy5EcMw1NMwgtSaaIdM0+SH7WXM/yEn3MvkkH5x/HFsdwZ3i4pwdNIoMwihQKSj+G2hFurCb5rE+PPpVr25dvbZTXSr3kRy9Vas/LwPryWK7KjB7HQPJjtqMNlRQyixp9LlByGRrsNqJ2R3Q1RKze1XDAw8bicVRTkYvjKS/eV085VziG8TFv9yDF85IW8l2wNxrDMz2GimsymUxuZgdzaXdWdJWcqvtmfS3VZOb2c5vd3V9Iry0yMqSA9XNT2cXjyNXMZj2nTpokh7FQyZbCqoYl1eFVm1lx3HOK0c1COKQcluTabUhtyBEoaUfMXwoo/oVbECgAprHAuTTmJFwhSyo4aonSPSwqKsIU5JzeWU1FzWV0TxTl4K7+Sl8HRWT57O6smgqAqOT8njmJQ80py+397gPjIDXgh4W2hrBgTttdtr+QKZ6YqB4adRnbUG01+JpTIPW8k2bKVbsRWtx160HoCgO4lAbC+Ccb0JRqfVFEcjQCXC9kEFw3YkEDT5fGMRL/yYy085lVgN+N2QRM4emUp/9SiUCLKFvCR6d5Ds3U73ohxiSjeQ7N1Gknc7jlDdb9WK7alsjB1Djqsfue7+5Lj6UeDsCYY+xIg0ymrHdMUTcsU1uEpK0E83fzmH+soxfOux+Jdg+MoprQ6ytTqKLb44tgRT2GTWFBOXVaTxXcWePXfjLVX0sJfXFBDdftLdIXq4vfRweulhN2gf3y2LCNR8kbw2r4q/L1zFC4t24Q2YGAb0TnAyKMVNRrxTY4+2EXeghMElX7N/8Rf0Lf8RCyH8hoNV8YezIuFINsaM1iXHIm1kYHQlV0Vv5YreW/mxNJZ38lL4ID+ZB7f24aGtvRkRW8rkpAKOSCykh6ulinqdhGEhFJ2KLzoVX/poCFRjK92GrWQb1tJtOHOWQM4STIudQGxPgrG9CcT1aje9D6Xt6D9aO1BSFeCNlfn8b3keueV+HFaDU4Ylc9bIVNLjnJEOT7oCM0RMoIB47y4SfDtrJ3PYSbxvF/HebGIDe44dUWJPYUfU/hQ4M8hz9SHX1Y9cd1+8Vs1cLNJqrHZMawJBV0KdxU5gUO2NoA+LLx/DvxW8ZeRXwfYqO1nVLnb4otkRiGOHmcJ2bzd+8vbALNmzmN/t+7dwWUI4XU6iXC5inFZinFY8TivRDivWNh8sSKTr2VxQxUfrivh4XRHbi2s+7Ma7bRyY7mZAsgu3XTPrtoWoQHG4SNinfAkWQgQNGxtjRvNT/GGsiTsEnzU60mGKdFkWA0bFlTIqrpSb+m3iq6IE3slL4auiBBaXxnHf5n7sF13OEbXFw4FRler8+2s2F4HEQQQSB4Fp1vQ+LN2GrWQrtuIt2Is3AxB0JRKM61XTA9GTHrHeh9J2VDCMoI35Vby8LJf31xTiDZgkR9u5aHx3ThqaTEJU+xx4VDogM0SUWUFMsBRPqISYUCmJfi+8/i3TNq0j1ruLeN/O8OQjv1RpjaXI0Z3t0cModPYk39WLyoQh7Agm4bOq16tEmkFHGGevzRulVgchdyK4EwFIqL0N3/14KIDFuwHD+yOB6jJ2VUBWtYMd1S6yAx62h1LY7k1hh5nCtkobUHdcVgMTj90gymUnxmkLFxI9jpqiYrTTiqWFX/TuzRkGmOavt93+3wMiTWGaJhvzq/hyUwmfbShmQ37NuL+JUTamH5TCGQcP5MOlm/EHdaFWa4vz7mRQwSL65H1K7/JlWAgRMOxsiB3L6rjDWBs3oV18Qaqih3Q6VgfY9r7DjAOYnFrB5NQKqoLb+LYolk/z4/m8IJ7Ht/Xm8W29yXBVc0RyMRMTSxgRV47DshfnVGsnHr7FMAhFd8MX3Q1f91G1vQ+3YyvdhrVkG46cpThylmJabARiehKM600gthemMzbSkUsrUMGwjfmDIb7ZXMr/lueFxyfcPzWK0w7qxhED47FbddmmNJ3d9OH5RSHQEyolJlRSWxys+T06VIaVemZuXgoZFicl9m5s9mRS7OhOsaM7Rc7uFDvSKXKk1fONuYHH48FXXo5GlpCIqf2EFB0V3SFmJXe0t/O6xRYuKBpA99rbKAAziGH48QwcyLcLvsNeth1vRTHlldXkBGPYYSbX9E4MdGN7WQo5ZXt+YDYM8DhtxLrsxLp3/7QT57IT47bhcdr2uqAYHb3n/tpdfkWaIRAyWZ5dzpcbS/hqUzHZpT+PS3jCAUlMGZxIZg8PNquFtLQEPlq2Bf3/bQVmiO5V6xiW9x088SMX564CIGA42JwwgfXJR7Ix8VB8tppzkL32FmlRDvXukc7BarUQDJm1k661jBjgpNqbPxhi4eZCPli1iw9X5fDvHS7+vSMNl93CuH5JHDowhUMHJdM/xdPkIR6CIbNr9LCzuQgkDiSQOLCm92FVPraSrVhreyDaS7YAEHQlEIztRSCut3ofdiIqGLaRLYVVvLmqgPdXF1JUFcBqgSmDEjjtoG4M7a7LGKQuwwwRFSqvLQKW4AmV7VEI9IRKcZn1z8plYlBh8VBuiSXHlk65JZYySyzl1jjKLLGEPOmccfYFPPTxFryB9l9wEamjdjxM/9ZF+KrLIxxME7iigQEYGO3/Y75hxYxOgiHHsvDbQrzWXhALxJhEmeX0CeQyKrCd5MAiUgK7iPIXkR/ysN2s6ZG4w0xhE+ls86Wxy5tAVvGevQQsmETbgsTagsRYA8Tag8TbAsTbA8Tagtgb+KbfYXfg89czgLnTBQzQLHrSYWSVeFmwtZSF20r5YXsZFb6a/8OpHjvTD0zhkH5xZPaIwWZV97HW5AhW0Ld8Cf1LFzKo9Dtidg+/EpXMquiD2ejanw3WvvgNJ5QCpYsjGm99HDFxQP9IhyGyz6yWmuFOnnjuRbyVrde2SwBOS4LcGBvbKp1srXLy5dpcPl+bB4DHGqR3lJeebi/pLh+xtvo/JzmjPMw8+/SuVxQzDEJRKfiiUqD7KAh4sZVtx1qyDVvpNhy5y3DkLsO02AjG9CQQ26tm7ENnw+NzS/umgmErqvAF+WRdEW/9VMDKnTWXdGXEOzk9sxvH7p9EcnR7+G5S2prVDNQU/YIlNUXA3bfgz70EPaEyLPX1CgR8OCiz1hQCyyxxlFtiawqC1ljKLTUFwQpLDCGj4X9gHkcsuBPA2NpaL1Ok1ZlmoGPMSm620KzkbcwM1c1vJW4qbb3ZYev9i5VMYkKlpAR20Te4kzGBlaQEPiYpmIeVICVmFDvMFDaTzhqjHxvJYIeZQk4ojlyviyxzz4Kixxog3uYnzh6oKSTa/MTbAyRag7UzvP+qKNhSs76LtJIKb5Afs8r4fmspC7eWsaOkZjxCiwH7p0YztncMh/SLZ1CKW5OXtCYzRPeq9fQvW0S/skX0rPgJKzXnj3xnL1YkTGFbyqGcPu10Ppz3Hwj68ft8YLbj/zOmvvSVzsXr8+Pz7zlMUktLsPhJ8FRxoAd8IYNsr4vt1TW3VWVRrCqLAmraJN2dXtKcXro7vCTa/TUXuvhaP8YOweYkkDCAQMIAvKaJpaqg9tLlrTVFxJItsB1CzngCcb0IxPYmGJMOFpWhOgodqRYWCJos2l7Kx+uK+GxDMVX+EE6bwTH7JTL1gCQOSm96N2fpeKwhHwmBvF8UAUt/URSsKRJGm/V/axYK9wqMY5etR22vwDjKrbW9A2vv+wynBq0RkfbBMCizxlFmjWMTg8OLLWaApGAeqYFsUgNZHBjIZkpgMU7TVzPkoAW8VjubbP1YbgxhNX3ZRDrZwXiKAw52+Zzs8NYdJ9WKSawtQLzdT4LdT4LNT6LdTzd99ybtTFGln2XZFSzNKmdZdjnr8ioJ1da502IcnDg0mbG9YhiZEUOsS03x1uTxF9Cv7Af6ly2ib9liooMlAPgsLjbGjmFjzGg2xoymyNmjZn2Xvev1GBLp4hwWkz7uKvq4a8aNLQ1Y2el1ssvrZKfXxfrKaNZX1lwR6DBCpDm9pEXBfit30b/aTqqpj2ZAbe/DZHxRyZA2AoJebKU7ai9d3oYjdzmO3OWYho1gTDqB3WMfuuIjHbk0Qq2UFhAMmSzJKufjdYV8vqGYkuqabyv3S43ihAOSmTIogWinGh+dgS1UTbwvhzjfrpoZhH05xPlzSPTnwMp8rqjIrfd5QayUWWIptCazzdqPMktczc0aF/693OLBbKRXoIhIRxEybOTZupNn685KRtYsNEMkBAtIqy0ipgay6RPYzn7m2vDzAtjIdXYnO6ona639WUNftoVSKA44KQk5KPJZ2FLlZnNVVPg5BibZLy3lxknd2/plimCaJlklPlbtqikQLs0uZ0vhz8OFxLttHNovnhE9PYztHUtGvFNfHLcid6CEXhXL6V2+jD7lS0mt3hR+bKd7AEtjjmVjzGi2Rx1AyKJvG0RkT7G2ILG2SgZHVwJQFbSwy7e7gOhkR7WLbdUGC59bDAwh3uZnP085+0dXsJ+nnAFRlfRyVTc4xEqXYXUSSOhPIKF/Te/D6kKsJVtreiCW7cBWug2AkDMufOly0NMDrDo3tycqGO6Dn3ZV8N6aQj5dX0RhZc3lCv2T3Zw+IoEjByWQEe+KcIR1ddQGak3YbRO7JeQnwb+TdF8hztLNxPlyiPftqvnp30V0oLje55XbkyG1H2vMDIpND6XWeMpqewSWWeOoNKLD466JSBfV6Mx/xi8ej3ADs7Vm/jMsFNlSKLKlsJoDa5aZJrGhYlIDWaQFskjz1/xMD2xnFN8B4MNOrq0Hue5eZEV3Z4e1J5vNNIoCTgr9dkqCLvokaSxg2TvNbRsVVfpZnVPJql0VrNpVwU85FZRW/3xZfGqMnaOH1ExUclCPGHontEyBsKO24aB123FRgWJ6lS+jd8Uyepcvq1MgLLclsCL+SDbGjGZTzCgq7IlNjFVE5Gdua4i+7ir61vZADJpQbonjoNGH8OP3n/FTqZ0fS2P5rjgh/BybEaKnq5p+7ir6R1XS111Fv6hK+riqibZFeCgVm4NItDVD9nRCMen4GQ9BH7aSbVhLtmAr3owjbwWOvBWYhoWgJ51gXC8Cif0h2I6HhOgiVDDcS4UVfi54aS0m0C85mrPH92Xq8O4MTI2JdGidTnSUp0VnQrUGq4n3ZhFXtYP46u3EV+8gvqrmZ4x31x5jB5oYlDtSKHb3ZptrHKXOdEqdaZS60il1dqfMmYrLFcVFh/XnvaefxVfVASZhEJE205yZ//acAziC2uJLDsOg1JpAqTWB9c6hNctqi4hpgR2k+bPoHthBWiCLnhVbGFH7NK/hJMeWzi5bT/I8/Thu/KHklGssQ2me5JQU7LaGm8IV3gCrd5aydHsxy3aUsGx7MdsKK8OPO2wWDkiP5cCe8RyUEc+oPgn0TIhqcHtdVYu140wTjy+H9LIV9ChZQs/SxSRX/lwgrLAnsSb5KLJiR7A9biRF7t7hCqBB086vmnVYRH6L1YAUV5DTRmdwbHE2ZnUpARM2V0axpiKajZVRbKpys7kyii8KE/m0MKnO8+NsftKdXnq6qkl3eunhqqaH00sPl5c+DitWk9b5jsVirWmPTrmxFTa+D0wT8tbCho8xNn+Bbcs32Mp24NzxLeamD7Gc8ipBd9Jvb0dahQqGeykhysac4/sxrF86Hy7dgq+6glcXboh0WI2KcdmZOWVoB/r2tCZQ39aF+KormvVMR6ia+GABCcH8mp+BfBKC+SQEC4gJleyxvs9wUGRNZpfzAIqsyZQ5UikgjhJrAqXWeELGL/5U/LW38mpgM7AZq2apE5EGNG3mPwOHw4HP5yPSPQw9cfFcOGNa5Lra/KKIuM45rGaZCSnWMpIqN9M9sJ20wA5SA1n08m+Gqq/g2R/h5P9FJl7pkAzDwG6z8fB7y/EGglT6guRX+Gtu5TU/S6rrFqET3DYGd3PTzeOgm8dOUrQdq8UAfKzdkcvaHfUPS9ISdrfh2uqKi5ax9+04AJvpI9WfRQ//VtJrbzGh0vDjZZZYVrky2W7vzzZHf4qsyTXnrQqgYivQ/IndNOuwiOwNmwEDoysZGF1ZZ7kvZLCt2sWm2iLi9io3WV4nWdUu1lREY9ZzTncYIZIdPpIdPlLs/pqfDh8JtgCxdj9xtkCdW7Q12LQmm1HbHp3/H7zVlb+9fptzAUdjSZ5Md99W+ga3MLpPAjj0RVwkqWC4lwzDYNKABNLS4nh7cQhfoP3PUuYNdNDeF2aw3plQnaFKEoIF4Vv8L3566plYpNpwUWRNZoe9N0XWpNpbMsXWJCoMzy8+HP/qg7vJb8+Qp1nqROQ3ND7znwGGUft4ZAuGPl87vPzDMCixJZPnimUNw2uWmSESg/n0JJdjfndmZOOTDmfJjjJWLC3mrWW55Ff4qfLX/T8e57LSN9FFUrSNlGg7ydF2HLa6vW6DIZNgqG3+XjtsGw4abMfVXadmjNPugR2k+7eRHthGt8BOrLVXfYQwyLemstE1hGx7L7bb+1BsSar7xYYZ3PfTp9pzItKCHBaTAVFVDIiq2uMxf8hgl9cZLiDu9DopNqPZWQl5Pgc7q12sLIsh9BtfFFkxibP7ibUGibX7ibYGibYGibKEcFuDRO2+uV0kLtzG8gIDI2jDboSwW0zsRgibYWI3TOyWEBYiPzzDZksvstyDGX3SeZg5ORDSuTlSVDCU9ss0oTyX7t5NeKq3kxAsDBcFE4IFuM09vxmpNKIpsiaxxTqQ4nBRsOZWbURF/uwnIiItw7BQaOtGubM3x/SZADk5kY5IOgjTNLnhrY2UeoNYjZqrRjLinSRF20mKspEYZcNu1bjDrcViBkkK5tTOop5Nqj+bbsGdOE1veJ0Kw8Mmx2Cy7b3ItvVil60nPktDY8CKiHQ8dotJhruaDHc1UIKBgcfjoby8HLP224+gCUV+O3k+B8UBGyUBG6V+OyW1v5cEbJT+4vddXicVQSuVQWu9vRdZuwKIbTQuC2ZNAdFSt5AY/t0IYbP84nfDDBce7YZZ+1jN705LCGftc/UxvGNSwVAiywwR688nwZdFojeLBG82ib7s2p9ZsLCK03/1lDJLDPm2bhRb6hYEi61JeC3uiLwMERER6RgMw+CfM4YQn5jE/xasb7Negl2OaULxdvpUrSK+civJgV0kB3NICezCxs89JqsNF7tsPci1pbPT1pNsey9KLAn6kldEujyrAckOP8mOhq5QqZ9pQnXIQmXQSmXtzypHIubQ6bz07kdU+QL4QwZ+08BvWvCHDAK7fzcN/CFL7X0Db8hCRdCK3zQImHv3ZZqFn4uH9d3cliAua81Pd23PSJv+NbcLKhhKq7OGfMT5c0nwZpPgyybBm0WiL4sE704SfNnYzLonQBODEns3dnn2o9fA4Xy5sZiCkKe2KJiI39A3zCIiIrL3eie6SEuLwWoxVDDcR9aQjwTfThK9O0j0ZpHk3Ua36i2keLfCwgpO/sW6ZZYYtjoGkGNLD99KLIkqDoqItCDDqJnd2W0NEZ4uJNpNzKAUFn/pw+fd8xLppjBNwoXEQG1h8de/+82aYqMvZMFb51ZTfCwLWMk37QSbUHx8/b5PeXL6IOJdmpAqUlQwlH1WUxDMId63izjfr3/uIjZQsMdzgoaNYkcamz0jKHKmU+joQZEznSJHOsWONIIWBx6XnauPGc5izTzcigyaM4D67va8YYBptl3jXp8jRESkdTTv/2DkRDBG0yQqUEycP4c4Xy5x/hwSvVm1tx3E+XMxfjV4YIUtntyoQWQMGcUnG0rZFYon35ZKtUWD14tIJ2J1gK2lOrMYv9heK3yRZXXs8yYMg5pLkcPx7f34ugETvCEL1UEr1SELVSEr1cGan1UhCz7sDMlIx23XECGRpIKhNMowg8T4C4jx5xHrzyfGn0eMP59Yf15tUTCHmHoKggDltgRKHKlsjx5GiSONIkcaRc4eFDp7UGpPwTT0TUHE1FbgoqOisQebP4hsdLSnpSNqVJRD7xUREWl5nmgPvr34P9jWolvj/6Bp4ghW4gkUEO0vxBMoIjpQiMdfSIy/gFh/brhA+OurQQB8FheFjh5kRw2m0NmTQmcPCh09KXD2pNKeEP7id7m++BWRTsZqtRAMmXiO+FOLb7vVP2UZ7aMAZzPAZg0Rba3/f7DD6ebqMzLJyckhpElPIkYFwy7KEgrg8ecTHSiqufmLiPHnh4uBsbW/ewKFe3xrvNvuguC22oJgsSOVYkda+PeAxdXGr0qarPYfhX/rInzVzWvEO+wOfH5fa0TV8D5j4oD+bbpPERHpvIzaL858W77D562OcDS/zeGJAfqHe9xbzAD2UDX2UDWO2p81v1fVWeYKlOEOluMKltb+LMMdKMMVLCM6WII91PBr91rclDhS2ezJpNSRSok9lRJHN0rs3ShyplNuS9IlACLSJVktVqwWgyeeexFvZUt9IWLgcDjw+Xy0Rg9DT1w8F86YpvO2NEu7LBgWFBRw6623snDhQqxWKyeccAI33HADNlu7DDfyTBNHqBJ3sAzXLxqDUYFiogPF4aJgbKgENlZxedEu3MHSBjcXwkKZPYkSRyo7og+g1J5MmT2ZUntKnZ9By753a5bIMs0AhALNeIYBZhBCQVqlq3xDTH2rJCLSlbRqW9A0if3sZijdxBl5+YRq/8fs+RGq7v+5tviIZZghrASxEMJihrAQxGIGseaZMOc6rvZ7sYQCWPfiMjATg2prNFXWGCptcRRF96PEiKPCFk+5PZEKW8LPP21JVFs9+mApItIIr8+Pz9+8CUkaZoBh1G6v5T9n+XzN+cwnUqNdVuCuuuoqUlNT+eqrr8jPz+fSSy9l3rx5XHDBBZEOrWWZJjbTiyNYhTNUWfttcFX4vj1UhSNUhTNYhSNUWVMIDJbjqv1m2BUsDxcJLTReUDExqLLFgqs7uVH9qXYmU4KHCls8FbYEKm3x4cJghS1BlwuLiIhIxLR2W9BSmQcVeUSFyjHNhj+YmXuUCVuzgGYSMiyYWAhgI2SxEqopHYLNQc/0nmwrqCJgWvBbnPgtbnwWF36Lq/a+C5/FXXvfhc/iotoaQ7XVQ5U1Fq816hftOwOPx0N5eTlt+gWgiIiIdBjtrmC4detWFi5cyJdffonb7SYjI4OZM2dy//33t6+CoWniXvMKrC7nsB1bMYJebKYfW8iHzfRhC/mw1v60NfDTEar6zUJffXwWF1XWGKqtMeTZk8K/V9lqGoXVtfdrioG7C4JxRLudXH3Mgbz09lLszig1EkVERKTdafW2oGFQfNw/SU1N5V9Pzdvr2SLbkscTy6zfn8X/3l6K17/3g8yLiIiINFW7KxiuX7+e+Ph4UlNTw8v69+9PdnY2paWlxMbGNmt7rXUZs+EtJXbdK4DJOAvwq7FDg1gIGXYCFg9Bw07IsBG02PEbdqoNO0GLA7/hImB14jectT9dBKwuAhZn+JviwC9++ixRtd8ON/01uWpvSUCUo+Z5GYkeLDYn1U5ozwXDKIcdgB5p3fF7K9t03zabnUCg6d3L3dExAPTo3h1/ddvGujf2Jd7m5qYldKT82mx27M6a8Ts7QrzQtvltifdPR3o/QNPjjcTfVn3aa34bys/uvze73d5lB8XubEO2tEVb0GKpabj1SE/H3wHGMHRHRwM1bThfoCUKhgYul7tV24KRbMftrV+e/8yAv12ckxsTqfP1vvy/aq//YxqyN/FG+v95e89xfflp7zH/WmvF25rvnY6W41+373aPPWy32xu9MqCrarW6l9nOsv3GG2/w0EMP8fnnn4eXbdu2jSlTpvDFF1+QlpYWueBEREREpFWpLSgiIiISee1jTu1fiIqKoqqq7qUhu+9H1367KiIiIiKdk9qCIiIiIpHX7gqGAwcOpLi4mPz8/PCyjRs3kpaWRkxMTAQjExEREZHWpragiIiISOS1u4Jhnz59GDlyJHfffTfl5eVs376dJ554gmnTpkU6NBERERFpZWoLioiIiEReuxvDECA/P5877riDBQsWYLFYOOmkk7j22muxWq2RDk1EREREWpnagiIiIiKR1S4LhiIiIiIiIiIiIhIZ7e6SZBEREREREREREYkcFQxFREREREREREQkTAVDERERERERERERCVPBUERERERERERERMJUMBQREREREREREZEwFQxFREREREREREQkrMMXDIuLi7n++usZO3Yso0ePZubMmeTm5gKwbNkypk+fTmZmJkcccQQvv/xyhKNte6tWreKss85i1KhRTJw4kbvuugufzwd07fwUFhYyZcoUFixYEF72W/l47bXXmDJlCgcddBCnnHIKS5Ysaeuw20R9ufnggw848cQTGTFiBEcccQSPPfYYoVAo/HhXyQ3Un5/dcnNzOfjgg3n11VfrLO/q+VmzZg3nnnsumZmZHHzwwcyZM4dAIBB+vKvkp77cvPPOOxxzzDGMGDGCo48+mhdeeKHOc7pCbtasWcP555/PmDFjmDBhAtdffz2FhYWAzsuN5Ubn5Z+pLdg4tQXrp7Zgw9QWbJzago1TW7BhagvWT23BhkW8LWh2cGeffbY5a9Yss6SkxCwrKzMvu+wy86KLLjKLi4vNMWPGmM8995zp9/vNb7/91szMzDSXLVsW6ZDbTDAYNCdMmGD++9//NoPBoLlz507z6KOPNh977LEunZ8ffvjBPPLII81BgwaZ33//vWma5m/m4/vvvzczMzPNH374wfT5fOYzzzxjjh071qysrIzkS2lx9eVmxYoV5vDhw81PP/3UDAaD5oYNG8zDDz/c/Ne//mWaZtfJjWnWn5/dgsGgec4555hDhgwx//e//4WXd/X8FBQUmGPHjjX//ve/mz6fz9y+fbt51FFHmU899ZRpml0nP/XlZu3ateaBBx5oLlmyxDRN01y8eLF5wAEHmIsWLTJNs2vkpqqqypwwYYL5yCOPmF6v1ywsLDQvvPBC8+KLL+7y5+XGcqPzcl1qCzZMbcH6qS3YMLUFG6e2YOPUFmyY2oL1U1uwYe2hLdihexiuXLmSZcuWcc899xAbG4vH4+HOO+/k2muv5cMPPyQ+Pp6zzjoLm83G+PHjmTp1Ks8//3ykw24zJSUl5OXlEQqFME0TAIvFgtvt7rL5ee2117j22mu5+uqr6yz/rXy8/PLLHHfccYwcORK73c55551HQkIC7777biReRqtoKDdZWVmcfvrpHH744VgsFvr378+UKVNYtGgR0DVyAw3nZ7fHH3+ctLQ0unfvXmd5V8/P66+/Tp8+fbj44oux2+307NmTp59+mmOOOQboGvlpKDdbtmwhEAiEz9GGYWC1WnE4HEDXyE12djZDhgxh1qxZOBwOEhISmDFjBosWLery5+XGcqPz8s/UFmyc2oJ7UluwYWoLNk5twcapLdgwtQUbprZgw9pDW7BDFwyXL1/OgAED+O9//8uUKVOYOHEi9957LykpKaxfv55BgwbVWX/AgAGsWbMmQtG2vYSEBM477zzuvfdehg0bxmGHHUafPn0477zzumx+Jk6cyEcffcSxxx5bZ/lv5WPDhg2dPl8N5eboo4/mpptuCt+vrq7m888/54ADDgC6Rm6g4fwAfP/997zzzjv89a9/3eOxrp6f5cuXM2jQIP7yl78wYcIEjjzySN58803S0tKArpGfhnIzceJEDjroIM444wwOOOAATj/9dK688kqGDx8OdI3c9OvXj6eeegqr1Rpe9sEHH3DAAQd0+fNyY7nReflnags2Tm3BPakt2DC1BRuntmDj1BZsmNqCDVNbsGHtoS3YoQuGJSUlrF27li1btvDaa6/x+uuvk5OTww033EBFRQVut7vO+i6Xi8rKyghF2/ZCoRAul4tbb72VpUuX8vbbb7Nx40bmzp3bZfOTkpKCzWbbY/lv5aMr5Kuh3PxSeXk5s2bNwuVycd555wFdIzfQcH4KCgq4+eab+dvf/kZ0dPQej3f1/JSUlPDqq68yfPhwPv/8cx577DFeeuklnnnmGaBr5Keh3Ph8Pnr27MkzzzzDsmXL+Mc//sGjjz7K119/DXSN3PySaZo89NBDfPbZZ9xyyy06L//Cr3PzS135vAxqC/4WtQX3pLZgw9QWbJzago1TW7Bhags2jdqCDYtUW7BDFwx3d9W95ZZb8Hg8JCcnc9VVV/HFF19gmibV1dV11q+urq73JN5ZffTRR3zwwQeceeaZOBwOBg4cyKxZs3jhhRdwu91dPj+/9Fv5UL5g06ZNnH766QQCAebPn4/H4wG6dm5M0+T666/nnHPOYejQofWu05XzAzXn6WHDhjFt2jTsdjtDhgzh7LPP5r333gO6dn4effRRHA4HBx98MHa7nUmTJnHcccfx0ksvAV0rN+Xl5VxxxRW89dZbPPfccwwePFjn5Vr15WY3nZfVFvwtags2nc45v03nnD2pLfjb1BZsmNqCP1NbsGGRbAt26ILhgAEDCIVC+P3+8LLds8Lst99+rF+/vs76GzZsYODAgW0aYyTt3LkzPAvebjabDbvdzqBBg7p8fn7pt/IxcODALp2vL774gunTp3PIIYfwr3/9i7i4uPBjXTk3O3fuZOHChTz++OOMGjWKUaNGkZ2dze23387FF18MdO38APTv33+P89Avx9LqyvnJzs6u8/8Lfj5HQ9fJzbZt2zj11FMpLy/nlVdeCTeCdF5uODeg8/Juags2Tm3BptM5p3E659RPbcHfprZgw9QWrKG2YMMi3hZsmflbIsPn85lTpkwxL7/8crO8vNwsKCgwf//735uzZs0yCwsLzVGjRpnPPPOM6fP5zO+++87MzMw0v/vuu0iH3WbWr19vDh061Py///s/MxAImNu2bTOPP/5485577lF+TLPODFW/lY/dMzJ999134VmGRo8ebRYVFUXwFbSeX+ZmyZIl5gEHHGC+/PLL9a7b1XJjmma9M+Ptdvjhh9eZGa+r52fDhg3m0KFDzSeffNIMBALmmjVrzEMOOcT897//bZpm18vPL3Pz3//+1xw+fLj55ZdfmqFQyFywYIGZmZlpfvrpp6Zpdo3cFBcXm5MmTTJvvPFGMxgM1nmsq5+XG8uNzss/U1uwcWoLNk5twYapLdg4tQUbp7Zgw9QWrEttwYa1h7Zghy4YmqZp7tq1y7zqqqvMCRMmmKNGjTKvv/56s6SkxDRN01y+fLk5Y8YMMzMz05w8eXKdE3dX8c0335jTp083R44caU6aNMl88MEHTa/Xa5qm8vPrf/S/lY/XX3/dPProo82DDjrInDZtmrl06dK2DrnN/DI3F198sTl48GDzoIMOqnP74x//GF6/K+XGNJvXSDRN5Wfp0qXmmWeeaY4aNcqcOHGi+fjjj5uhUCj8eFfKz69zM3/+fPOoo44yMzMzzeOOO85844036qzf2XPz9NNPm4MGDTIPPPDAPc4xptm1z8uN5Ubn5brUFmyc2oINU1uwYWoLNk5twcapLdgwtQXrUluwYe2hLWiYZm1fYBEREREREREREenyOvQYhiIiIiIiIiIiItKyVDAUERERERERERGRMBUMRUREREREREREJEwFQxEREREREREREQlTwVBERERERERERETCVDAUERERERERERGRMBUMRURaWVlZGYWFhZEOQ0REREQiQG1BEemIVDAUkYh5/vnnGTx4MPPmzYt0KM2yY8cOBg8ezI4dO5q0/pQpU1i/fv1e72/w4MEsWLCg3sfOOecchg4dSmZmJpmZmRx44IEcc8wxPPnkk4RCoTrr5uXlcccdd3DEEUdw4IEHMnHiRK655hrWrVsXXue2225jwoQJFBQU1HluIBDgtNNO4+KLL8Y0zb1+LSIiIiK7qS3YNGoLikgkqGAoIhHz/PPPc8YZZzB//nwCgUCkw2k1RUVFrbr9iy++mCVLlrBkyRJ+/PFH/vKXv/D8889z++23h9fZsWMHJ598Mnl5efzjH/9gyZIlvPHGG2RkZHDaaafxzTffAHDTTTeRnJzMTTfdVGcfjz76KPn5+dx7770YhtGqr0dERES6BrUFW4bagiLSGlQwFJGI+O677ygoKODGG28kFArxwQcfhB8rLCzk2muvZfTo0YwdO5arr76akpISALZv384ll1zCyJEjGT9+PLfddhs+n6/eb3offfRRzjnnHABeffVVzjzzTO69917GjBnDuHHjePbZZ/nvf//L4YcfzsiRI/nLX/4Sfu4RRxzBq6++Gr6/YMECBg8eXO9r+fHHH/n973/PxIkTGTZsGKeccgpLly4F4Oijjwbgwgsv5J///CcA3377LdOmTWPUqFEcd9xxvPnmm+Ft+f1+5syZw9ixYxk3bhxPPfVUs/JqtVoZP348d999Ny+99BKbNm0CYM6cOQwcOJC5c+cycOBALBYLSUlJXHXVVZxzzjnceOONBAIBnE4nDz30EIsWLeLZZ58FYOHChcybN4+HH36Y+Pj4ZsUjIiIiUh+1BdUWFJH2TQVDEYmIZ599ltNOOw2Xy8WZZ57J008/HX7syiuvpLy8nA8//JBPPvmE0tJSbr/9dgKBAH/84x9JSUnhyy+/5O2332bp0qU8+uijTdrn4sWLSU1N5fvvv+eKK65gzpw5LFiwgHfffZd58+bxyiuvsGjRoma9jurqai699FKOPvpovvzySxYsWECvXr247777AMKN33/+859ceOGFrFmzhksvvZSLLrqIBQsWcOedd3L33Xfz1VdfAfDEE0/w+eef88orr/Dpp5/WuUSkOQ4++GAcDgcLFizA5/PxxRdfcOKJJ9b7jfBpp51Gbm4uS5YsAaBfv3785S9/4YEHHmD16tXceOONXH/99QwfPnyvYhERERH5NbUF1RYUkfZNBUMRaXNZWVl89dVXnHXWWUBNI2XDhg0sXLiQrKwsFi5cyA033EBCQgIej4d77rmHSy+9lB9//JGsrCxuvvlmoqOjSUpK4rHHHmP69OlN2m9UVBTnnnsuFouFiRMnEgwG+eMf/4jb7WbYsGF069aNrKysZr0Wu93OSy+9xJlnnonP5yMrK4v4+HhycnLqXf/FF19k8uTJHHXUUVitVkaMGMFpp53G888/D8Abb7zBH//4RzIyMoiKiuLPf/7zXl32YRgGcXFxFBcXU1RUhN/vJzk5ud51u3XrBkBubm542cknn8xRRx3F6aefTmZmZvhYiYiIiOwrtQXVFhSR9s8W6QBEpOv5z3/+QyAQ4MQTTwwvCwQCPP3001xyySUA9OjRI/xYSkoKKSkpvPPOOyQkJOB2u8OP9ezZE6BJg07Hx8eHG1wWS833JbGxseHHLRbLHoND/xar1cqCBQu48MILqaysZMCAAdhstgYHg87KyuL7779n1KhR4WXBYJBevXoBNQ217t27hx+LjY0lLi6uWTFBTT5LS0tJSkoiISEBm81GdnZ2vevuzl1KSkqd5ZdddhlvvPEGV155ZbP3LyIiItIQtQXVFhSR9k8FQxFpU16vl1deeYXZs2dz8MEHh5evW7eOiy66iAsvvBCA7Oxs+vTpA8CGDRt4++23OeSQQygqKqKqqircUPzhhx9YuXJleHwYv98f3uavB5huzrezFoul0W3ttmzZMu68805efPFFhg4dCsDTTz/N5s2b610/LS2Nk08+mTvuuCO8LDc3N9yoTEtLY/v27eHHKisrKSsra3Lcu3377bd4vV7Gjx+Pw+Fg8uTJvPrqq0ybNi3cQN7t5ZdfJiUlhczMzDrLd6/36/VFRERE9pbagmoLikjHoL98EWlTb731FoZhMHXqVNLS0sK3Qw89lEGDBvH6668zYcIE7rvvPkpLSykvL+f+++9n+/btDB8+nD59+nDvvfdSVVVFfn4+c+bMobCwkKSkJOLi4njnnXcwTZNVq1bx/vvv73Wc/fv355NPPqG6upq8vDzmz59f73plZWVYLBZcLhcAS5cuZf78+fh8vvA6Docj3NCbNm0ab7/9Nl9//TWhUIgtW7Zw9tlnh8ftmT59Ok899RQbN27E6/Vyzz33EAwGmxx3IBDgyy+/5C9/+QvnnnsuGRkZANxyyy1kZ2dzxRVXsHHjRkKhEDk5OTz44IP85z//4e6778Zut+9VrkRERESaSm1BtQVFpGNQwVBE2tR//vMfpk6dWm+DZMaMGbzxxhvcd999eDwejjnmGCZPnkxiYiK33347drudv//97+Tk5DBp0iROPPFERo8ezRVXXIHD4eDOO+/kvffeY8SIEdxzzz2cdtppex3ntddeS0VFBRMmTOD3v/89J5xwQr3rTZgwgTPPPJOzzjqL0aNHc/vtt3POOedQWFhIfn5++HVdc801PPTQQxx44IE8+OCDPPjgg4wePZqzzz6bI444gmuuuQaomUHvhBNO4Oyzz2bixInExMT85mx0//jHP8jMzCQzM5ODDz6Yhx9+mIsuuoibbropvE5qaiqvv/463bt356KLLmLEiBGceuqp7Nixg//+978ceuihe50rERERkaZSW1BtQRHpGAyzocEVREREREREREREpMtRD0MREREREREREREJU8FQREREREREREREwlQwFBERERERERERkTAVDEVERERERERERCRMBUMREREREREREREJU8FQREREREREREREwlQwFBERERERERERkTAVDEVERERERERERCRMBUMREREREREREREJU8FQREREREREREREwlQwFBERERERERERkTAVDEVERERERERERCRMBUMREREREREREREJU8FQREREREREREREwlQwFBERERERERERkTAVDEVERERERERERCRMBUMRkXosX76c3//+94wYMYLx48fzpz/9iZycnPBjM2bMIDMzk+OOO473339/j+eXl5czadIkMjMz6yzfvn07l1xyCaNHj2bkyJGce+65rF69uk1ek4iIiIiIiEhTGKZpmpEOQkSkPSkqKuLoo4/G7/czY8YMduzYwUcffURmZib/93//x1FHHYVpmpx88sl8+umnZGVl8cILL4SLgxs3buS6665j1apVREVFsWTJkvC2p02bxtq1aznllFMwDINXX32Vbt268fHHH0fq5YqIiIiIiIjUoR6GIiK/snHjRnr27Mn555/PjTfeyKOPPkp8fDxLly7lgw8+oLS0lLPOOotbbrmF66+/HtM0ee211wB48803Of7446murq5325s2bSIxMZHjjz+eY489lvj4eBwOR1u+PBEREREREZFG2SIdgIhIezNq1CheffXV8P1169ZRWlpKr1692LBhAwB9+/YFoH///gCsX78eAJfLxQ033MAZZ5zB8OHD99j2rFmzeOCBBzj77LMBSEpK4vHHH2/V1yMiIiIiIiLSHOphKCLSiDVr1nDBBRcQCoW46qqrqKysBAj3CnQ6nQDh5UcddRTnnXdeePmvde/enZiYGGbNmsWf/vQniouLue666wgGg23wakRERERERER+mwqGIiIN+PLLLznzzDPJz8/n1ltv5dhjjyUqKgoAn88HEL70ePfyxhQUFHD99dfTt29frrjiCi6++GIOPfRQVqxYwaJFi1rvhYiIiIiIiIg0gy5JFhGpx/vvv88111yD3W7nscceY/LkyQD069cPgC1bttT5OXDgwN/c5o4dO/D7/ZSVlYWX/brHooiIiIiIiEikqWAoIvIr27dv5/rrrycQCDBu3DjWrl3L2rVrgZpZju+//36ee+45Kisr+fTTTzEMg5NOOuk3tzt48GC6devGhg0buPzyy/F4PCxYsIA+ffowdOjQVn5VIiIiIiIiIk1jmKZpRjoIEZH25IEHHuDJJ5+s97HvvvuOrKwsZs+ezdq1a+nevTuXXXYZxx577B7rDh48mKioKJYsWRJetmXLFh588EEWLFhAKBRi5MiR3HLLLWRkZLTa6xERERERERFpDhUMRUREREREREREJEyTnoiIiIiIiIiIiEiYCoYiIiIiIiIiIiISpoKhiIiIiIiIiIiIhEWkYPjdd98xffp0RowYwYQJE7jzzjuprq4G4K9//StDhw4lMzMzfHvppZciEaaIiIiItKDCwkKmTJnCggULGlzniy++YOrUqRx00EEcc8wxfPbZZ20YoYiIiIhABAqGhYWFXHzxxZxxxhn88MMPvPbaayxcuDA8I+mKFSu48847WbJkSfg2Y8aMtg5TRERERFrQ4sWLmTFjBtu2bWtwnS1btnD55Zdz5ZVX8sMPP3D55Zdz1VVXkZOT04aRioiIiEibFwwTExP59ttvOeWUUzAMg+LiYrxeL4mJifh8PtatW8fQoUPbOiwRERERaSWvvfYa1157LVdfffVvrjdq1CiOPPJIbDYbxx57LKNHj9bVJiIiIiJtLCKXJHs8HgAOO+wwpk6dSkpKCqeccgpr1qwhEAgwd+5cDj74YI4++miefPJJQqFQJMIUERERkRYwceJEPvroI4499thG19uwYQODBg2qs2zAgAGsWbOmNcMTERERkV+J6KQnH374IV9++SUWi4UrrriCsrIyxowZwznnnMMXX3zB/fffz7PPPsvTTz8dyTBFREREZB+kpKRgs9l+c72KigrcbnedZS6Xi8rKytYKTURERETq8dstt1bkcrlwuVxcd911TJ8+nQceeID58+eHHx8+fDjnnnsu7777LhdccMFe7aO4uJhAINBSIXcKhmGQlJREQUEBpmlGOpx2R/lpmHLTOOWnccpPw5Sbxik/jbPZbMTHx0c6jBbhdrvDE+HtVl1dTXR09F5vU23Bjk1//52HjmXnoOPYeehYdh6t1RZs84Lhjz/+yM0338ybb76Jw+EAwOfzYbfb+eabbygtLeX0008Pr+/z+XC5XHu9v0AggN/v3+e4OxPDMADw+/06MdRD+WmYctM45adxyk/DlJvGKT9dx6BBg1i1alWdZRs2bNin8a3VFuzY9PffeehYdg46jp2HjqX8lja/JHnw4MFUV1fzwAMP4PP5yMrK4t5772XatGnY7XbmzJnDd999h2maLFmyhPnz52uWZBEREZEu4IQTTmDhwoW8++67BAIB3n33XRYuXMiJJ54Y6dBEREREupQ272EYHR3NU089xd13382ECROIiYlh6tSpzJo1C4fDwU033cRtt91GTk4OycnJXH755WokioiIiHRSmZmZ3H777Zxwwgn079+fxx9/nL/97W/ccsst9OjRg0cffZS+fftGOkwRERGRLiUiYxgOGDCgwYlMTj/99DqXJIuIiIhI57F27do695csWVLn/iGHHMIhhxzSliGJiIiIyK9EdJZkERERERERERERaV9UMBQREREREREREZEwFQxFREREREREREQkTAVDERERERERERERCVPBUERERERERERERMJUMBQREREREREREZEwFQxFREREREREREQkTAVDERERERERERERCVPBUERERERERERERMJUMBQREREREREREZEwFQxFREREREREREQkTAVDERERERERERERCbNFOgARaVmGYbT6tlt6H6Zptuj2RERERERERGTvqWAo0okkp6Rgt7X+n3VqamqLbs8fCJCfl9ei2xQRERERERGRvaOCoUgnYRgGdpuNh99bjjcQbK294In2UF5RDrRMr0CnzcpVxwzHMAz1NBQRERERERFpB1QwFOlkvIEgvkColbZu4AuGarev4p6IiIiIiIhIZ6RJT0RERERERERERCRMBUMREREREREREREJU8FQREREREREREREwlQwFBERERERERERkTAVDEVERERERERERCQsIgXD7777junTpzNixAgmTJjAnXfeSXV1NQDLli1j+vTpZGZmcsQRR/Dyyy9HIkQREREREREREZEuqc0LhoWFhVx88cWcccYZ/PDDD7z22mssXLiQJ598kpKSEi666CJOOukkFi1axOzZs5kzZw7Lly9v6zBFRERERERERES6JFtb7zAxMZFvv/0Wj8eDaZoUFxfj9XpJTEzkww8/JD4+nrPOOguA8ePHM3XqVJ5//nmGDx/e1qGKiIiIiIiIiIh0ORG5JNnj8QBw2GGHMXXqVFJSUjjllFNYv349gwYNqrPugAEDWLNmTSTCFBERERERERER6XLavIfhL3344YeUlJRw7bXXcsUVV5Camorb7a6zjsvlorKycq/3YRgGhmHsa6idyu58KC/166j5+Tleo/bWGvv4+adpttQ+Oma+69NR3zttRflpmHLTOOWnccqLiIiIiLS0iBYMXS4XLpeL6667junTp3POOedQVlZWZ53q6mqio6P3eh9JSUn7GmanlZqaGukQ2rWOmh9PtAdfMNSq+4iO9rTYthzWmo7OHTXf9elMr6U1KD8NU24ap/yIiIiIiLSNNi8Y/vjjj9x88828+eabOBwOAHw+H3a7nQEDBvDNN9/UWX/Dhg0MHDhwr/dXUFCA3+/fp5g7G8MwSE1NJScnB9M0Ix1Ou9NR87M77vKKcnyB1ikYGkZNsbCiopyWSo3DVlMw7Gj5rk9Hfe+0FeWnYcpN45Sfxtntdn1BKiIiIiItqs0LhoMHD6a6upoHHniAa665hry8PO69916mTZvG0UcfzQMPPMC8efM466yzWLx4MW+99RZPPPHEXu/PNE19uGiActO4jpsfs/bWCluuvQy5Ji0ttQ+zdpsdNd976kyvpTUoPw1Tbhqn/NRPORERERGRltbmBcPo6Gieeuop7r77biZMmEBMTAxTp05l1qxZOBwOnn76aWbPns3cuXNJTEzkz3/+M+PGjWvrMEVERERERERERLqkiIxhOGDAAJ5++ul6Hxs2bBgvvvhiG0ckIiIiIiIiIiIiAJZIByAiIiIiIiIiIiLthwqGIiIiIiIiIiIiEqaCoYiIiIiIiIiIiISpYCgiIiIiraqgoICZM2cyatQoxo4dy+zZswkEAvWu++9//5sjjjiCESNGMHXqVD744IM2jlZEREREVDAUERERkVZ11VVXERUVxVdffcUrr7zCd999x7x58/ZY74svvuAf//gHTz31FD/++COXXXYZV111FTt27Gj7oEVERES6MBUMRURERKTVbN26lYULF3LdddfhdrvJyMhg5syZPP/883usu2nTJkzTDN+sVit2ux2bzRaByEVERES6LrW+RERERKTVrF+/nvj4eFJTU8PL+vfvT3Z2NqWlpcTGxoaXH3fccbz66qsce+yxWK1WDMPg/vvvJy0tba/3bxgGhmHs02uQyNl97HQMOz4dy85Bx7Hz0LHsPFrrGKpgKCIiIiKtpqKiArfbXWfZ7vuVlZV1CoZ+v58hQ4Ywe/ZshgwZwltvvcUtt9xC//79GTx48F7tPykpae+Dl3bjlwVn6dh0LDsHHcfOQ8dSGqKCoYiIiIi0mqioKKqqquos230/Ojq6zvI777yTESNGMHz4cABOPfVU3n77bV577TVuvPHGvdp/QUEBfr9/r54rkWcYBqmpqeTk5GCaZqTDkX2gY9k56Dh2HjqWnYfdbm+VL0hVMBQRERGRVjNw4ECKi4vJz88nOTkZgI0bN5KWlkZMTEyddbOzsxk6dGidZTabDbvdvtf73z0eonRsOo6dh45l56Dj2HnoWHZ8rXX8NOmJiIiIiLSaPn36MHLkSO6++27Ky8vZvn07TzzxBNOmTdtj3SOOOILnnnuOVatWEQqFeP/991mwYAHHHntsBCIXERER6brUw1BEREREWtXcuXO54447mDx5MhaLhZNOOomZM2cCkJmZye23384JJ5zAZZddhtVq5fLLL6ekpITevXvz+OOPs99++0X4FYiIiIh0LSoYioiIiEirSk5OZu7cufU+tmTJkvDvNpuNyy+/nMsvv7ytQhMRERGReuzTJckbN24kJyenpWIRERERERERERGRCGtWwfDHH3/kpJNOAuDFF1/kuOOOY/LkyXz88cetEZuIiIiIiIiIiIi0sWZdkvzAAw8wadIkTNPkH//4B/fccw/x8fE88MADHHnkka0Vo4iIiIiIiIiIiLSRZvUw3LRpE1deeSWbNm0iPz+fY489lkmTJrFjx47Wik9ERERERERERETaULMKhlarlYqKCr788ksOOuggHA4HWVlZeDye1opPRERERERERERE2lCzLkk+8sgjOfvss8nKyuLPf/4zGzZsYNasWRx//PGtFZ+IiIiIiIiIiIi0oWYVDG+99VbeeOMNXC4Xxx57LFu2bOH000/n97//fWvFJyIiIiIiIiIiIm2oWQVDq9XKKaecQklJCStXrmT//ffnrLPOwmq1tlZ8IiIiIiIiIiIi0oaaNYZhRUUF11xzDWPHjuXss89my5YtTJkyhU2bNrVWfCIiIiIiIiIiItKGmlUwvO+++6isrOS9997DbreTkZHB4YcfzuzZs5u10zVr1nD++eczZswYJkyYwPXXX09hYSEAf/3rXxk6dCiZmZnh20svvdSs7YuIiIiIiIiIiMjeaVbB8LPPPuOee+6hb9++GIaB3W7nxhtvZMWKFU3eRnV1NRdccAGZmZl8/fXXvP322xQXF3PzzTcDsGLFCu68806WLFkSvs2YMaN5r0pERERERERERET2SrMKhqFQCIfDAYBpmnssa4rs7GyGDBnCrFmzcDgcJCQkMGPGDBYtWoTP52PdunUMHTq0OWGJiIiIiIiIiIhIC2nWpCfjxo3jjjvu4C9/+QuGYQDw8MMPM2bMmCZvo1+/fjz11FN1ln3wwQcccMABrFmzhkAgwNy5c1m8eDExMTGceuqpXHDBBVgszapthhmGEY5VauzOh/JSv46an5/jNWpvrbGPn3+aZkvto2Pmuz4d9b3TVpSfhik3jVN+Gqe8iIiIiEhLa1bB8KabbuLSSy9l9OjRBINBMjMz6dOnD3//+9/3auemafLwww/z2Wef8dxzz5Gfn8+YMWM455xzePDBB1m9ejWzZs3CYrFwwQUX7NU+kpKS9up5XUFqamqkQ2jXOmp+PNEefMFQq+4jOtrTYttyWGu+DOio+a5PZ3otrUH5aZhy0zjlR0RERESkbTSrYJiUlMRLL73EihUryMrKIi0tjeHDh2O1Wpu94/Lycm666SZWrVrFc889x+DBgxk8eDATJkwIrzN8+HDOPfdc3n333b0uGBYUFOD3+/fquZ2VYRikpqaSk5MTvrRcftZR87M77vKKcnyB1ikYGkZNsbCiopyWSo3DVlMw7Gj5rk9Hfe+0FeWnYcpN45Sfxtntdn1BKiIiIiItqkkFw+zs7Dr3k5OTSU5OBmo+5AOkp6c3eafbtm3jwgsvJD09nVdeeYXExEQAPv74Y/Lz8zn99NPD6/p8PlwuV5O3/WumaerDRQOUm8Z13PyYtbdW2HLtZcg1aWmpfZi12+yo+d5TZ3otrUH5aZhy0zjlp37KiYiIiIi0tCYVDI844ojw+Di/bJQahoFpmhiGwerVq5u0w5KSEs4991zGjRvH7Nmz64xNaJomc+bMoXfv3owbN46lS5cyf/58brrppua8JhEREREREREREdlLTSoYfvLJJy22w1dffZXs7Gzee+893n///TqPLVmyhJtuuonbbruNnJwckpOTufzyyznxxBNbbP8iUj9vIER+hZ/iqgCl1UHKvUGqAyG8gRCh2u8JLAZEOYuwGyYep5V4t414t40Ujx2bRYPui4iIiIiIiHQGTSoY9ujRI/x7SUkJn332Gbm5ufTo0YPDDjsMj6fpEyCcf/75nH/++Q0+fvrpp9e5JFlEWkeVP0R2iZesUh+7Sn2UeYN1HjcMcNksOG0WHLUdgUMmlFb5qfQH64xhaDEgxWOnR5yTvoku4t3NGh5VRERERERERNqRZn2qX7x4MZdeeilut5u0tDSys7OZM2cOzzzzDAMHDmytGEWkhVT6gmwurGZLYTU5Zf7wKIQxTiv9k1wke+wkum3EumxEOyzhoQh+ZuDxeCgrK6PcF6SkKkBBpZ9dpT5yyvzklPn5cUc5iVE2BneLYkCyKzwLsoiIiIiIiIh0DM0qGN5999384Q9/4JJLLgFqxhx87LHHuOOOO3j22WdbJUAR2Tch0ySrxMfa3Eq2FXkxAasFeiU46RXvJD3OicfZvJnODcMgxmklxmmlZ7yTA9MhFDLJLvWFC5LfbSnlh21lDO7mZnh6NG5782dTFxEREREREZG216yC4aZNm7jgggvC9w3D4JJLLmHevHktHZeI7KMKX5C1uZWsy6uiwhcCoEecg0EpbjLindhbuOefxWLQM95Jz3gn43rHsLGgmp92VbJyVyVrcqvYPzWKA9OjcdjU41BERERERESkPWtWwbBv374sWbKE0aNHh5etX7+eAQMGtHhgIrJ3iqsCLN5exob8KkImRNktHNQjmkEpbmKcbTO2oN1qYUi3KAanuNlS5OXHHeUs31nB+vwqRmfEMCDZVc/lziIiIiIiIiLSHjSrejB27FguueQSTj31VHr37k1ubi4vv/wyY8aM4bHHHguvd9lll7V4oCLSuDW5ldz5yY+8syIXgG4eO8O7R5OR4MQSoeKcYRj0TXTRJ8HJurwqfthexpebSlifV8Uh/WPbrIApIiIiIiIiIk3XrE/rK1euZP/992f16tWsXr0agP79+1NQUEBBQQGAeg2JtLH1eZX8/dtsvt1SCtSMTTg0LZq0GHu7+Xs0DIPB3aLok+jih+1lrMmt4rXlBYzrE8MBadGRDk9EREREREREfqFZBUNNbCLSfmwvruaf3+3ko3VFAEwemMCfjjmAtxdvwhcIRTi6+jltFib0jaN3gouvNpXw1aZSdpX5ucIXiHRoIiIiIiIiIlKrWQVDv9/Pu+++S1ZWFqHQzwUJwzCYNWtWiwcnInvKr/Dz9IKdvLkqn2AIxveO5eKD0xmSGk1aWhxvL450hL+tZ7yTU4Yn8+XGmsuTT3r8G+48uje9EpyRDk1ERERERESky2tWwfCaa65hwYIFDBw4sM6ljioYirQ+fzDES0vzeGbBTir9IYZ1j+bSCelk9oiJdGh7xWmzcOSgeH7KqWThtjL++OIa7j6uL6N7xUY6NBEREREREZEurVkFw6+//po333yTnj17tlY8IlKP77eW8tAX29lW5KVHnIO/HtKTQ/rFtZsxCveWYRhk9ozhsiP355Jnf+DqNzZw/eG9OGFocqRDExEREREREemymlUwTElJIT4+vpVCEZFfyy7x8siXO/hyUwlOm8HF47tzxohUnDZLpENrURMHJvPPGYP50xsbmPPJNvIq/PxhTFqHL4iKiIiIiIiIdETNKhjecMMNXHnllZx55pnExta9bHD06NEtGphIV1btD/HsD7t4bnEOvqDJEQPjuXxiT9JiHZEOrdX0TXLzVG3R8Knvd1LuDXLFIT1UNBQRERERERFpY80qGC5btoxvvvmGb775ps5ywzBYvXp1iwYm0hWZpskXG4t55MssdpX56Jfk4urDMhiV0THHKWyuxCg7j50yiOve3MiLS3Kp8AW5cXIvLCoaioiIiIiIiLSZZhUM//Of//Dkk08yceJELJbOdUmkSKRtKaziwc93sGh7GdEOC1cd2pNTh6dgs3atYpnHaeWhkwZw8zubeGtVAYCKhiIiIiIiIiJtqFkFQ6fTyYQJE1QsFGlBFd4g/1q4k/8uzSUYguP2T2LmwekkRtsjHVrEuOwW5hzfjxvfrikaWgyD64/IUNFQREREREREpA00q/J3/vnn87e//Y2SkpLWikekywiZJu+uLmDG/FW88GMug5KjeGrGYP48pXeXLhbu5rRZuOf4foztFcMbK/N57KusSIckIiJ7qaCggJkzZzJq1CjGjh3L7NmzCQQC9a67cOFCpk+fTmZmJocddhj/+Mc/2jhaEREREWlWD8Pnn3+e7Oxs5s2bt8djGsNQpOnW5lbyt8+3s3JnBfFuGzdN7sXxBySpB92vOG0W7pnan6tf28ALS3JJ9tg5c0RqpMMSEZFmuuqqq0hNTeWrr74iPz+fSy+9lHnz5nHBBRfUWW/jxo1cdNFF/PWvf+Wkk05i7dq1nHvuufTu3Zvf/e53EYpeREREpOtpVsHwnnvuaa04RLqEkqoAf/82mzdW5mMYMP3AFC4Y151YV7P+FLsUl83CvVP7cekr63j0qyySouwcPSQx0mGJiEgTbd26lYULF/Lll1/idrvJyMhg5syZ3H///XsUDP/zn/8wefJkTj75ZACGDBnCiy++iMfjiUToIiIiIl1Ws6oUY8aMqXd5YWFhiwQj0lkFQyZvrMzn799mU+YNclAPD9cc1pMBKVGRDq1DiHXZeOjEAVz437Xc+dEW4t02xvaOjXRYIiLSBOvXryc+Pp7U1J97iPfv35/s7GxKS0uJjf35fL58+XIOPvhg/vSnP/HNN9+QmJjIeeedx4wZM/Z6/4ZhYKgHf4e1+9jpGHZ8Opadg45j56Fj2Xm01jFsVsFw+fLl3HfffeTk5BAKhQDw+/0UFhaycuXKVglQpKNbllXOA19sZ31eFSkeO9cdnsGRgxJ0Ym6mbjEOHj5pABe/vI6b39nE46cOYkiqCq4iIu1dRUUFbre7zrLd9ysrK+sUDEtKSpg/fz4PPfQQ9913H0uWLOHiiy8mLi5ury9JTkpK2vvgpd34ZcFZOjYdy85Bx7Hz0LGUhjSrYHjHHXeQkZHBwIED2b59OxMmTGD+/Plcc801rRWfSIeVV+7j8a+z+GBtETaLwTmjUjlvdBpRDmukQ+uw+ia5uf+E/lzx6nr+9MYGnjxtMD3jnZEOS0REGhEVFUVVVVWdZbvvR0dH11nucDiYPHkykyZNAmD06NGceOKJvPfee3tdMCwoKMDv9+/VcyXyDMMgNTWVnJwcTNOMdDiyD3QsOwcdx85Dx7LzsNvtrfIFabMKhuvXr+e5555jx44dzJ49m/PPP5/MzEzuuOMOzj///CZvZ82aNdx7772sWrUKu93OhAkTuPHGG0lMTGTZsmXcddddbNiwgYSEBC699FKmT5/e7BcmEin+YIiXlubxzIKdVPpDjO8dy9WH9SQjwRXp0DqFA9M93HFMX256exM3vL2RJ08bTLSKsCIi7dbAgQMpLi4mPz+f5ORkoGZyk7S0NGJiYuqs279/f3w+X51lwWBwnz7ImKapD0KdgI5j56Fj2TnoOHYeOpYdX2sdP0tzVo6NjcXlcpGRkcH69esBOOigg8jKymryNqqrq7ngggvIzMzk66+/5u2336a4uJibb76ZkpISLrroIk466SQWLVrE7NmzmTNnDsuXL2/eqxKJkO+3lnL286t5/OssEqJs3De1Hw+c2F/FwhZ2WP94Zk7swaaCau74YAsh/YMTEWm3+vTpw8iRI7n77rspLy9n+/btPPHEE0ybNm2PdU8//XQ++eQT3njjDUzTZNGiRbz11luceOKJEYhcREREpOtqVsGwX79+vPDCCzidTqKioli9ejUbN25s1lhs2dnZDBkyhFmzZuFwOEhISGDGjBksWrSIDz/8kPj4eM466yxsNhvjx49n6tSpPP/8881+YSJtaVtRNde+uYGrX99ATpmPi8d35/mz9+eQfvEaq7CVnDWiG0cPTuDLTSX88/udkQ5HREQaMXfuXAKBAJMnT+a0007jkEMOYebMmQBkZmby5ptvAjB+/HieeOIJ5s+fz8iRI7npppu44YYbmDx5ciTDFxEREelymnVJ8pVXXsmll17KhAkT+OMf/8hpp52G1WrljDPOaPI2+vXrx1NPPVVn2QcffMABBxzA+vXrGTRoUJ3HBgwYwCuvvNKcMEXaTJk3wNMLdvHyslyCIZgyKIGZE3qQFuuIdGidnmEY3Hhkb7YVeZm3cBcDkt1MHpgQ6bBERKQeycnJzJ07t97HlixZUuf+YYcdxmGHHdYWYYmIiIhIA5pVMBwxYgRffvkldrudGTNmsN9++1FWVsaECRP2auemafLwww/z2Wef8dxzzzF//vw9ZtFzuVxUVlbu1fahpqigHl51afr0xu3Oi8ViaXAsgGDI5I0Vefzju2yKqwLslxrFnyb1Yni6py1DrePn42nU3lpjHz//NM2W2sfevx/ddiv3Tu3PeS+s5q4Pt9Ir3sWgbpGbOVl/W41Tfhqm3DRO+Wmc8iIiIiIiLa1ZBUPTNHE6a2Yk3bJlC7t27WLMmDF7tePy8nJuuukmVq1axXPPPcfgwYNxu92UlZXVWa+6unqPGfSaozVmiuksNH16w4Ihk27dutX72Lcb8rnj7Z9Ys6uMbjFO/jb9AE7J7IHF0j4+sHmiPfiCoVbdR3R0yxVGHdaakRH29v2Ylgb/PDeGM578npve3cKbl00gyRPZmZP1t9U45adhyk3jlB8RERERkbbRpIJhWVkZV155Jenp6dx11118/fXXXHLJJXg8HgzD4D//+Q99+/Zt8k63bdvGhRdeSHp6Oq+88gqJiYkADBo0iG+++abOuhs2bGDgwIHNeEl1FRQU4Pf79/r5nZGmT2+cxWKhW7duPPL+Cqr9wfDykqoA320pYXNhNVYDRvT0MKJnDBuz87k/Oz+CEdeIcdmZOeUAKirL8fpbp2BoGDXFwoqKclrqreOw1RQM9+X92MMJ1x+RwV0fbeXify/gkZMHYo1AAVd/W41Tfhqm3DRO+Wmc3W7XF6QiIiIi0qKaVDB85JFHCAQCnHvuuQD87W9/4+STT+bOO+/kmWee4ZFHHuHhhx9u0g5LSko499xzGTduHLNnz8Zi+XnelSlTpnD//fczb948zjrrLBYvXsxbb73FE0880fxXVktThDdMuanf7pxU+4P4AkG8gRDLsitYtauCkAl9Ep2MyYghxmXDNE18geBvbLFteAM1BbKa8FvnuO6+DLll92HWbnPf3o/H7Z/Eql0VvLYin6e+z+ai8ektFF/z6W+rccpPw5Sbxik/9VNORERERKSlNalg+Omnn/Lss8/So0cP8vPzWbNmDXPmzAHglFNO4cknn2zyDl999VWys7N57733eP/99+s8tmTJEp5++mlmz57N3LlzSUxM5M9//jPjxo1rxksSaRmBkMmKnRUszSrHFzRJjLIxrncs3TWhSbt15aE9+SmngnkLdzGsezTj+8RFOiQRERERERGRDqdJBcOioiJ69OgBwLJly3C73QwZMgSAmJgYqqqqmrzD888/n/PPP7/Bx4cNG8aLL77Y5O2JtLSQafLqjzt44cccyr1Boh0WxvWOpX+yC4sGlm/XnDYLdx/bj/NeWMPtH2xh3hn7acZqERERERERkWay/PYqEBUVRXl5OQA//PADmZmZ4Rn5tm/fTmxsbOtFKNJGTNPk+62l/P75n/jTf5fhD4YY0yuGaQemMDDFrWJhK9s9o/m+3nrEu7j1qD6UVAf583ubCYTMFtv2L28iIiIiIiIinVWTehhOmDCBuXPnctxxx/HWW28xa9YsoKbA8q9//YuxY8e2apAire2nXRX837fZ/LC9DIfV4OJD++H3VmG00liA8jOrxSAYMlt09tPT0tLYVAp//2Ij/1pcxG0nHNBi297NHwiQn5fX4tsVERERERERibQmFQz/9Kc/8Yc//IH58+czbtw4pk+fDsCkSZPwer289NJLrRqkSGtZl1fJP7/bydebSzCA4/ZL5KKDe3DgwF7c+9bSdjOhSWdmtRhYLQYPv7cCbyDQYts1TZPusQ7mfbuF7IIS+ie7W2zbTpuVq44ZjmEYmmxAREREREREOp0mFQzT0tJ45513KCoqIjExMbz8sssuY9KkSaSkpLRagCKtYVNBFU99v5PPNhQDcPiAeP44tjv9k911Zu6WtuMNBPEFQi26zUn943htRQGfbygm3mUlxtWkU56IiIiIiIhIl9bkT8+GYdQpFgLhnoYiHcXWomr+9f1OPl5XhAkc0i+OC8Z1Z1BKVKRDk1YQ5bByWP84PlhbxGcbSjh+/0QsFo0/KCIiIiIiItIYdbeRLmFrUTXzF+3i/TWFhEwY1zuWC8d1Z/+06EiHJq2sZ7yTYd2jWbGzgsU7yhndKybSIYmIiIiIiIi0ayoYdjAtMTvr7m201UyvkRzjbV1eJf9etIvP1hdjAiN7xnDR+O4MT/dELCZpeyN7ethV6mP5zgq6xzroGe+MdEgiIiIiIiIi7VaTCoZffPEFhx12WGvHIr8hOSUFu63larwtOSttYyIxm+zy7HL+vWgX324pBWB871jOHZPGgSoUdklWi8GkAXG8vrKALzeVcPKwJNx2a6TDEhEREREREWmXmlR9uvbaa1m0aBFHHXUUH374YWvHJPUwDAO7zcbD7y3Hu88z9xp4oj2UV5QDrdv7ry1nkzVNk0Xbyvj3D7v4cUc5BjWTmZw7Oo3B3TRGYVcX67IxsW8sn20o4YuNJRw9OKHNetmKiIiIiIiIdCRNKhja7XZmz55NdnY2jz32WL3rXHbZZS0amNSvZWaSNfAFQ7Xbidzlwi0lEDT5ZH0RLy7JZU1uJVYDjt0vkXNGpdIn0R3p8KQd6ZfkJqvEx7q8KlbsrNCl6SIiIiIiIiL1aFLB8NZbb+Xll1/GNE0WLFiwx+PqpSORUO4N8sbKfF5emktOuR+nzeCU4cmcPTKV7rEao07qN653DLllPn7YUU5arINuHkekQxIRERERERFpV5pUMDzmmGM45phjmD59Os8++2xrxyTSqJ2lXv67NI83V+VT6QuRGGXjovHdOXlYCvFuzeMjjbNbLRw+MJ43Vxbw2YYSThqahNNmiXRYIiIiIiIiIu1Gs6orL7/8MhUVFXzxxRdkZWXRrVs3Dj/8cGJjY1srPhGgZnzCZdkV/G95Hp+tLyJoQr8kF2dkpnLU4AQcKvhIMyRG2RnbO5Zvt5TyzeZSDh8Qp57SIiIiIiIiIrWaVTDcunUr5513Hn6/n/T0dLKzs7n33nv597//zcCBA1srRunCyrwB3l9dyGsr8tlcWA3AmF4xnDEilbG9YlTkkb02pJub7BIvmwur6ZHn0MQ4IiIiIiIiIrWaVTCcM2cOv/vd77juuuuwWCyEQiHuv/9+7rnnHv71r3+1VozSBa3OqeC1Ffl8tLaI6kCIGKeV0w5K4eRhyZrIRFqEYRhM7BdH3op8vttaSjePg4QoXdIuIiIiIiIi0qxPx8uWLWPu3LlYLDWXf1osFq688komTpzYKsFJ11LlD/LR2iJeW5HPmtxKAA5Ii+LkYSlMHpiAy67LjqVlOW0WDh8Qzzs/FfLZhmJOGJqEzaJeqyIiIiIiItK1NatgaLVaKS8vJzExMbysvLwct1s9vmTvbcyv4rUV+by/poAKXwi33cKJQ5M5eViyLhOVVpca42BETw+Ld5SzYGspE/rGRTokERERERERkYhqVsHw8MMP55prruHWW2+lZ8+ebN++nbvuuovDDz+8teKTTsobCPH5hmJeW5HHsuwKAPonuThleApHD04k2mmNcITSlQxPj2ZnqY81uVV0j3XQL0lfgoiIiIiIiEjX1ayC4TXXXMPll1/OscceG55s4rDDDuPaa69tleCk89leXM3rK/J556cCSqqDOKwGvxuSyCnDkxmaFq1JTCQiLIbBYf3jeH1lAV9vKiU52k6sS+MZioiIiIiISNfUrE/E8fHxPPvss2zfvp2CggJ69OhBSkpKa8UmnYQ/GOKzDUW8tjyPhdvKAMiId3Lu6DSO3S+JOLcKMxJ5UQ4rk/rH8d6aIj5dX8zUA5KwajxDERERERER6YL2qlKTkZFBRkZGS8cinUy5N8iGrHIm3PMpuWVerBY4fEA8Jw9LZlRGjHoTSruTHufkoB7RLM2qYOG2Msb3iY10SCIiIiIiIiJtTl27pEWFTJOsEh9rcirZXuzFBHrEu7n44HSO3z+J5Gh7pEMUaVRmDw+7Sn38lFNJ91gHfRJdkQ5JREREREREpE1ZIrnzwsJCpkyZwoIFC8LL/vrXvzJ06FAyMzPDt5deeimCUUpTVPmDLMsq5+Vl+Xy4tohtxV56xjs5Zr9Evrz+cM4f013FQukQLIbBpAHxuGwGX20qoaw6EOmQRERERERERNpUs3oYvvPOO0yZMgWHw7HPO168eDE33ngj27Ztq7N8xYoV3HnnnZx88sn7vA9pXaZpsqvMx+qcKrYWVRMywW23cGB6NIO7RRHjtOKwWTQOnHQ40Q4rh/WP54O1RXy2oYTj9k/U+1hERERERES6jGb1MLz99ttbZNy51157jWuvvZarr766znKfz8e6desYOnToPu9DWo8/GGJ1TiWvrijg3dVFbC6sJjXGwRED4plxUAqjMmKIcVojHabIPukZ72R492jyKvz8sL0s0uGIiIiIiIiItJlm9TAcNmwY7777LieeeOI+7XTixIlMnToVm81Wp2i4Zs0aAoEAc+fOZfHixcTExHDqqadywQUXYLHs3dXThmF0isk1fn4NRu1tX7b180/TbPq2SqoCrM6tYF1eFf6gid1qcEBaFEO6RRPf4EzHRu2+Os4x2B1rzY+OE/fPuf759xbfw16+d35jq7/YdvvK98ieMewq87FyVyXdY530Stg9nmH97+uf3zvt63W0F8pPw5Sbxik/jVNeRERERKSlNatgWFxczA033MCtt95KcnJynQbqJ5980uTtpKSk1Lu8rKyMMWPGcM455/Dggw+yevVqZs2ahcVi4YILLmhOqGFJSUl79bz2yhPtwRcMtci2oqM9v7mOaZpsLqhg+fYSthZWApAU7WB4zziGpMXisDVeyHVYax5PTU3d94DbWHSUB3sL5botRDtqenW2RdxNee80eVttGPfeOO5AF/9ZsI2vNpVwxpg4Yt3233xfd8T3e1tSfhqm3DRO+RERERERaRvNKhieffbZrRUHABMmTGDChAnh+8OHD+fcc8/l3Xff3euCYUFBAX6/v6VCjBjDMEhNTaW8ohxfYN+KKoZRU/CpqCjHNOtfJxAy2ZBfxcqd5ZRUBzGAPgku9k+LIi3GgWEY+Kor8f3GvnYXFHNycjAb2lk7Y7FY6NatGxWV5Xj97a+A1SBXzZ9za8bdlPdOs7VB3PvCAhzaL46P1hXx5tIdHH9AMlG1Rc5fv693/512pPd7W1J+GqbcNE75aZzdbu90X5CKiIiISGQ1q2D4y4lICgsLSUxMbNFgPv74Y/Lz8zn99NPDy3w+Hy6Xq5FnNc40zU724cKsve3DFmovJa1JS91teQM14xP+lFNJlT+Ew2owvHs0+6VG4akzLmFTYzBr99VxjsPuOOvLT/vW+nE39t7Zh622wjZbVq8EJ5k9olmSVcG3m0uYPCgeaPh93ZHe75Gg/DRMuWmc8lO/jpCTgoICbr31VhYuXIjVauWEE07ghhtuwGZruCm6bt06pk+fzpNPPsnYsWPbMFoRERERadbAgIFAgIceeoiRI0dyxBFHsH37dk499VTy8vJaJBjTNJkzZw7fffcdpmmyZMkS5s+fz4wZM1pk+9Kwcm+Q77eW8uKSPBbvKMdiwNheMczITGF0r5hfFQtFup7MHh56xjlYn1/FT7sqIx2OiEiHctVVVxEVFcVXX33FK6+8wnfffce8efMaXL+qqoprrrmG6urqtgtSRERERMKaVTB89NFH+f7773nkkUfCl7+kpaVx1113tUgwU6ZM4aabbuK2224jMzOT6667jssvv3yfJ1mRhhVXBfhiYzH/XZrHql2VxDitHNovjtMOTGFo9+jwWG0iXZ1hGEwaEE+M08rXm0v4cVtRpEMSEekQtm7dysKFC7nuuutwu91kZGQwc+ZMnn/++Qafc/vtt3PkkUe2YZQiIiIi8kvNuiT5rbfe4oUXXiA1NRXDMIiKimLOnDlMmTJlrwNYu3Ztnfunn356nUuSpXUUVfr4dmMxG/OrMIHuMQ6Gp0fTI86h2RZFGuC0WZg8MJ63fyrk0ucW8/SMwSRGNes0KiLS5axfv574+Pg6k9b079+f7OxsSktLiY2NrbP+66+/ztatW5k9ezZPPPFEW4crIiIiIjSzYFhZWRket3D3eDkulwuLRb3QOorS6gBLsyrYUFCFaUJ6rIMRPT2kxjhadb8dqQjZkWKVtpcUbeew/nF8sr6YP7+7ibknD8Rm1XtGRKQhFRUVuN3uOst236+srKxTMNy4cSMPPfQQL7zwAlZrywyHYhiG/rd3YLuPnY5hx6dj2TnoOHYeOpadR2sdw2YVDA866CAee+wxrr766nBAzz77LMOGDWuV4KTlVPmDLMmqYE1uJaYJPRPcHJgWRVqsvVX3a7UYBENmnV4FIh3doG5RZKTEM+/bLfzt8+3ccESG/tGKiDQgKiqKqqqqOst234+Ojg4v83q9XH311dx8882kp6e32P41g3TnoLZk56Fj2TnoOHYeOpbSkGYVDG+55RbOPfdcXnvtNSoqKjj22GOpqKjgmWeeaa34ZB/5gyFW7qpkRXYF/pBJN4+dURkxDExPory8nNaeldZqMbBaDB5+bwXeQKBV99VSYlwOZk45ANV/pDG3HLcfP+0o4I2V+fROcHLGCP2jFRGpz8CBAykuLiY/P5/k5GSgpidhWloaMTEx4fVWrFjBli1buOWWW7jlllvCyy+55BJOPPFEbrvttr3af0FBAX6/f59eg0SOYRikpqaSk5PTIWYEl4bpWHYOOo6dh45l57F7jpGW1qyCYUZGBu+88w6ff/45WVlZpKWlMWnSJDweT4sHJvsmZJqsz6vixx3lVPpDxLqsHJoRQ+8EJ4bR9peQewNBfIFQm+93b3gDwUiHIB2A3Wph9rH9uPC/a3n0qyx6xjs5tH9CpMMSEWl3+vTpw8iRI7n77ru54447KCoq4oknnmDatGl11hs1ahTLly+vs2zw4MH8/e9/Z+zYsXu9f9M09UGoE9Bx7Dx0LDsHHcfOQ8ey42ut49fsypHT6aR79+5kZGTQp08fFQvboZwyH2+uLODrzaWETBjfJ5ZThyXTJ9GlyyZFWlCMy8bfTuhPnNvGX9/fwrrcykiHJCLSLs2dO5dAIMDkyZM57bTTOOSQQ5g5cyYAmZmZvPnmmxGOUERERER+qVk9DLdu3crFF1/Mjh07iI+Pp6ioiP3335/HH3+cbt26tVaM0kRV/iCLtpWzPr8Kw4Bh3aM5KD0ah02T0oi0lh5xTu45rh+Xv7aea9/cwNt9Wm7cLRGRziI5OZm5c+fW+9iSJUsafN7atWtbKyQRERERaUSzKkl33nkn48aN44cffuDrr79mwYIFDBgwgDvuuKO14pMmCJkmK3dW8PKyfNbnV5Ee6+CUYcmM6RWjYqFIGziwh4ebj+xFbrmfC+b/QJVfl7WLiIiIiIhIx9WsHoYrVqzgiSeewOFwAODxePjLX/7CpEmTWiM2aYKCCj9fby4hvyKAx2Hh0H5xteMU6tJjkbb0uyFJbCvy8szCXdz09ibum9oPu1UFexEREREREel4mvVptkePHmzbtq3Osl27dhEfH9+SMUkTBEImi7aV8cbKAgoqAgzvHs2pw1M0TqFIBF00Pp3TR2fw/dZSbvtgC8GQBg8WERERERGRjqdJPQxff/11AEaMGMGFF17IH//4R3r06EFubi5PP/00Rx55ZGvGKL+ys9TH15tLKK0OkhRlY2K/OJKj7ZEOS6TLMwyD2ScPI6+4nE/WFxHj3MYNR/RSEV9EREREREQ6lCYVDH85SLVhGDz99NN1Hn///fe57rrrWjYy2UOVL8jXm0pYsbMCqwGjM2IY2j0Ki4oRIu2G1WLw16P7UO4N8MbKAmKdNmZO7BHpsERERERERESarEkFw08//bS145Df8NOuCmY/v4aNeRWkxtg5tF8csa5mDUEpIm3EYbMw5/h+XPHaBp5dnEOMy8o5o9IiHZaIiIiIiIhIkzS74vTDDz+QlZWFadYdm+ukk05qqZjkFwJBk38v2sUzC3ditVgY1zuG/VLVq1CkvXPbrTxwQn9mvrKOJ77JxmYxOGNEaqTDEhEREREREflNzSoY/vWvf+WVV16hW7dudcbkMgxDBcNWsL24mtve38JPOZX0T3bz2FmjePOHjfgCoUiHJiJNEOuy8cjJA7n81fXM/SqLKn+I88ekaUxDERERERERadeaVTB89913eemllxg6dGhrxSO1PlxbyL2fbqPKF+LskalcND6dXt1jeTPSgYlIsyRF23n81IFc9foG/vn9Tqr9IS6dkK6ioYiIiIiIiLRbzSoYxsTEMGjQoNaKRYAqf5AHP9/B2z8VkBRl457j+jG6V6yKCyIdWEKUncdOHcif3tjIs4tzqAqEuPqwnhpaQERERERERNolS3NWvvTSS7nllltYs2YN2dnZdW6y7zbmV/GHF9fy9k8FjO0Vw/wz92N0r9hIhyUiLSDGaeORkwYwsqeHV5blcffH2wiGzN9+ooiIiIiIiEgba1YPQ6/Xy7vvvsvbb78dXmaaJoZhsHr16hYPrit556cC7vu0poAwc0I6Z41MVe8jkU4mymHlbycO4JZ3NvHOTwUUVfq585i+RDmskQ5NREREREREJKxZBcMnnniCP//5z0ycOBGLpVmdE6UBvkCIh77Ywesr80n12Lnz2L4M6+6JdFgi0kpcNgv3HN+POZ9s473VhVz6yjruP6E/3TyOSIcmIiIiIiIiAjSzYBgMBjnjjDNaK5YuZ1epj5vf3cTqnEpGZ8RwxzF9iXc365CISAdkt1q4dUpvesQ5eer7nfzxxbXMOa4fQ7tHRzo0ERERERERkeaNYXjKKacwf/781oqlS1m4rZTzX1zD6pxKzh2dykMnDVCxUKQLMQyDP47tzu2/60OZN8DM/63jzZX5kQ5LREREREREpHk9DJcvX84zzzzDI488QlxcXJ2Zez/55JMWD64zMk2TF5bk8vjXWbjtFu49vh+H9o+PdFgiEiFHDU6kd4KLG9/exJxPtrEsu5xrJmVoXEMRERERERGJmGYVDKdNm8a0adNabOeFhYXMmDGDu+66i7FjxwKwbNky7rrrLjZs2EBCQgKXXnop06dPb7F9RpI3EOLeT2vGLeub6OLe4/uRkeCKdFgiEmGDu0XxzBlDuPPDLby7upCVOyu445i+DO4WFenQREREREREpAtqVsHw5JNPbrEdL168mBtvvJFt27aFl5WUlHDRRRdxxRVXMGPGDBYtWsSsWbMYPHgww4cPb7F9R0JBhZ8b397Eyl0VTOgby+1H9yXaqR5EIlIj3m3j/hP689KSXJ74JpsLXlrLH8amcc7INGxWzZguIiIiIiIibadZBcNzzjmnzmXIv9ScsQ1fe+015s6dy3XXXcfVV18dXv7hhx8SHx/PWWedBcD48eOZOnUqzz//fIcuGK7NreT6tzaSW+7nnJGpXHxwOlaLCgAiUpfFMDhjRCojesZwx4dbePK7nXy5sYSbjuzFoBT1NhQREREREZG20ayC4e7LhncrKiri/fffZ8aMGc3a6cSJE5k6dSo2m61OwXD9+vUMGjSozroDBgzglVdeadb2f8kwjAaLnA0tb0kfrS3kzg+3YJomt/2uL8fsl7RX2/k5VqP2tvd2b8owwDRbOwfGL/bZMYqkP78t9j3Xbav1c906752O9x75Oea68e6+v6/nliGp0cw7Yz/+tWAnz/6wi/NfWMP0A7tx0fj0FumZ3Bbnvsb2a7FYME2zyc9rzrodVUu9dzor5adxyouIiIiItLRmFQwvu+yyPZadcsop3Hfffc3aaUpKSr3LKyoqcLvddZa5XC4qKyubtf1fSkqqv0AXDJmt2ssvFDJ5+ON1zP10E91inPzjnJFk9krY5+16oj34gqEWiBCioz0tsp1G91E7cUN0lAd7C8Xd2qLCMUd3mJihbXPdku+djvgecVhrJphPTU2t9/GGljfX7T3TOW18CX9+fSUvLc3lkw0l/GnKIE4b1RObtVmT3Ie19rmvKbp169as9dtDzG2lpd47nZXyIyIiIiLSNppVMKzPAQccwMqVK1siFtxuN2VlZXWWVVdXEx0dvdfbLCgowO/311lmGAapqak8/N4KvIHgXm+7If5giE/WF7G5oJoUj52jByfw4bKtfLhs615vM8ZlZ+aUA6ioLMfr37eiimHUFHwqKspp9Y47rpq3WEvE3VYM9+6YK/D6W/790WraINet8t7pgO8Rh62mWJeTk1On99vuc8uvl++LJAs8fnI/3lpVwN+/zeLm11bwzy/Wc+H4dCYNiMfSjJ5FrX3u++391xSGKyqb/v5x2qxcdcywFs1pe9Qa753ORPlpnN1ub/ALUhERERGRvdGsgmF2dnad+36/n3feeYfu3bu3SDCDBg3im2++qbNsw4YNDBw4cK+3aZpmgx8uvIEAvkDLFiiq/EE+WltMXoWfvokuDu0fh80Cvn38cO4N1BQFal7Kvn1Y2n0paUtsqwl7a8N9tYyf3y4mHSXmGq2f69Z573S898jPMdd/fmnsvLM3DOCEA5KYPDCe5xfn8MKPudz8zib6J7k4b0wakwYkYGtGD7zWOPc1jYE9GKotDDc1P43nurPpKq9zbyk/9VNORERERKSlNatgeMQRR9QZJ8c0TeLi4rjrrrtaJJgpU6Zw//33M2/ePM466ywWL17MW2+9xRNPPNEi229txVUBPlhbRLk3yIHp0Yzs6dG4QiLSYqIdVi4an860A1P4z485/G9ZPre+t4W0mGxOOyiFY/dPIs61zx3HRUREREREpItr1ifLTz75pM59q9VKUlISdru9RYJJSEjg6aefZvbs2cydO5fExET+/Oc/M27cuBbZfmvaWerl43XF+IMmE/vGMribZjQVkdaRGGXnsok9OWtEKq+uyOd/y/KY+1UW//dtNhP7xnHMfomM7x2HzaovLERERERERKT5mlUw7NGjR4sHsHbt2jr3hw0bxosvvtji+2lN6/Oq+HpzCVaLwdFDEugR54x0SCLSBSRE2fnj2O6cPTKVT9cX8c5PhXy+oZjPNhST4LYxZXAChw9IYGj36GZdstwedcTe2rpMVEREREREOqomFQx/fSnyrxmGwccff9xiQXUUpmmyNKuCH7PKiXZYOGpwAolRLdPbUkSkqZw2C8fsl8Qx+yWxq9TH+2sKeHd1If9dmsd/l+YR47QyplcMB/eN54TofZ+tvS1ZLQbBkNkhZ8f1BwLk5+VFOgwREREREZFma1LB8PLLL693+dKlS3nppZfYf//9WzSojiAYMvlmcwnr86tJiqrpyRPtsEY6LBHp4tJiHZw3pjvnjk5jdU4l324p4dstpXyyvphP1hdz54dbSHDbSPHYSfHY6eaxk+C2tdsefFaLgdVi1M7sHIh0OE1WM7vzcAzDUE9DERERERHpcJpUMDz55JP3WPb000/zv//9jzPOOIObbrqpxQNrz7yBEJ+sL2ZnqY+MeCeHD4jDbrVEOiwRkTDDMNg/LZr906K5YFw6hRV+FmwrY2mOj8/W5LAur4p1eVUA2K0GiVE2Ety1t6iaIqLL3n7Oa95AMEIzO4uIiIiIiHQ9zZ5Os7S0lBtuuIEffviB+++/n2OOOaY14mq3yrwBPlxbTHFVgP26RTGuTwyWdtozR0Rkt8RoO8fun8Qfjkjj3reWUFzpJ7e85pZX7qOwMkBOmb/Oc5w2gxinlRinreany1p734rHYcXSwcdFFBERERERkfo1q2C4dOlSrr76ahISEnj11VfJyMhorbjapbxyPx+tK6LKH2JMrxiGpkW128v4REQaE+Ww0ifRSp9EF1AzJmulP0RRZYCiqppbWXWAMm+Q/IrqPZ5vAFEOCx6HlejaAqLHaSXaYcFTe99haz89FEVERERERKTpmlwwfOqpp3jkkUeYMWMG119/PQ6HozXjane2Flbz+cYSTNPkiIHx9K39kC0i0hkYhkG0w0q0w0rP+LozvQdCJuXeIGW7b9UByr1Byn1BSqqD5JT7692mw1qzzd0FxGinleRYE5vpx+Ow4HZY1ENbRERERESkHWpSwfCSSy7hiy++4Oyzz+aoo45i2bJle6wzevToFg+uvVi1q4Lvt5bhslmYMiiBbjFdq1gqIl2bzWIQ77YR767/X0YgZFJRW0As9wap8AUp94bC97NKvITC836UhZ9nAB6nlTiXlTi3jTiXLfx7lN2iHtwiIiIiIiIR0qSC4eeffw7As88+y7PPPrvH44ZhsHr16hYNrD0ImSYLt5WxalclcS4rRw1OINbV7GEfRUQ6NZvFqCn4NVBQNE2TKn+Icl+IoGEnv7SipoeiN0ipN0h2qY8dJb46z7FbDOLcVrrFOEiI3Ux2iZdYp0WXOYuIiIiIiLSBJlW/1qxZ09pxtDv+YIjPN5awrchLWoydIwcl4NQHVRGRZjMMgyiHlSiHDY/HQ/doAwh3OSRk1lzyXFIdpKQqQEl1gJLqIMVVAX7aVcltb/0UXjfGaSUpykZitJ2kKBvdPI52NZuziNSvoKCAW2+9lYULF2K1WjnhhBO44YYbsNn2bIq+8MILzJs3j9zcXLp168bvf/97zjrrrAhELSIiItJ1qbtcPSp9QT5aV0R+RYD+SS4O6ReHVbOBioi0CothEOuyEeuykfGr8RMtFoND98/g8Y/XkFfuo6AywNYiL1uKvOF14lxWunnsdPM46BZjJ95t09iIIu3MVVddRWpqKl999RX5+flceumlzJs3jwsuuKDOeh9//DEPPvgg//znPznwwANZunQpF110EcnJyRx99NERil5ERESk61HB8FcKK/18uLaICl+IzB7RZPbwaBwtEZEIiXJYOWRgCt+uycIXCAI1YyYWVfrJqwiQW+Yjt9zP+vxq1ufXzOZstxikeOykxTpIj3WQEm3Hoi99RCJm69atLFy4kC+//BK3201GRgYzZ87k/vvv36NgmJOTw4UXXshBBx0EQGZmJmPHjmXRokUqGIqIiIi0IRUMf2FHsZdP1xcTNE0O7RfHwBR3pEMSEZFfsVkMUjwOUjwO9k+NAqDKHyS3zE9uuZ/cch855T6yS338WLt+Woyd7rFO0uMcJEapB6JIW1q/fj3x8fGkpqaGl/Xv35/s7GxKS0uJjY0NL//1pccFBQUsWrSIm266qc3iFREREREVDMPW5FTy7ZZS7FaD3w1OpHusZkIWEeko3HYrvROt9E50ARAMmeRV+NlZ4iO71PvzxCrbwWE16B7roGe8k55xTjxOa4SjF+ncKioqcLvrfgm7+35lZWWdguEv5eXlcfHFFzN06FCOP/74vd6/YRi6WqQD233sdAw7Ph3LzkHHsfPQsew8WusYdvmCoWmaLNpexoqdlcQ4a2ZCjm9gpk8REekYrBaDtBgHaTEOMvEQCJrk1vY6zC7xsa3Iy9bacRDj3TYy4h30jHOSGuPQmLUiLSwqKoqqqqo6y3bfj46Orvc5S5cu5corr2TUqFHMmTOn3slRmiopKWmvnyvtxy97qErHpmPZOeg4dh46ltKQLl0Z8wdDfLq+mC1FXrp5amZCdmu2TRGRTsdmNUiPc5Ie54QM8AZCZJV42VHsZUeJjxU7K1mxsxK7xaB7nINe8U56JThx29X7UGRfDRw4kOLiYvLz80lOTgZg48aNpKWlERMTs8f6r7zyCnfddRdXXHEFf/jDH/Z5/wUFBfj9/n3ejkSGYRikpqaSk5ODaZqRDkf2gY5l56Dj2HnoWHYedru9Vb4g7bIFw9yyat5cWUBuuZ++iS4O7R+HTb1KRES6BKfNQr8kN/2S3JimSUFloKZ4WOxle5GXbUVe2AzdPHZ6JzjpleBS73ORvdSnTx9GjhzJ3XffzR133EFRURFPPPEE06ZN22PdDz74gNtuu43/+7//45BDDmmR/ZumqQ9CnYCOY+ehY9k56Dh2HjqWHV9rHb8u+elnU0EV1837idxyPwemRzOyp2ZCFhHpqgzDIDnaTnK0nYN6ePAGQmwv9rK1qJqs4ppZmBdtLyfOZaVXgoveCU5SPHZNnCLSDHPnzuWOO+5g8uTJWCwWTjrpJGbOnAnUzIR8++23c8IJJ/DYY48RDAa54oor6jx/6tSp3HHHHZEIXURERKRL6nIFw0DQZOYr6yj3Bjl8QDz9klyRDklERNoRp83CgGQ3A5LdBEImO0t9bCuqZmuRlxU7K1ixswKXzUKvBCe9E5z0iHNq3EOR35CcnMzcuXPrfWzJkiXh39966622CklEREREGtHlCoY2q8GZI1KZNLQXn63cii8QinRIIiLSTtksBhnxTjLinRzcp2bm5d0TpqzLq2JdXhV2q0GveCd9El30jHdqeAsREREREenwulzBEOD3o9NIS0vks5VbIx2KiIh0EIZh0M3joJvHwaiMGEqqA2wprGZLoZeNBdVsLKgOFxgHprip8AYiHbKIiIiIiMhe6ZIFQxERkX0V57JxYLqHA9M9lHkDbCn0sqWwms21txF3fsS43rEcPiCeiX3jiHZqxmUREREREekY2mXB8N133+Xaa6/F6XSGlx155JHcf//9EYxKRESkfjFOG8O62xjWPZoKX5AdJT78ppWvNhXyxcZi7FaDMb1iOHxAAhP7xRHnapf/fkVERERERIB2WjBcsWIFJ554InPmzIl0KCIiIs0S7bAyrHs0N0zNZNXG7XyxsYjPNxTz/ZZSvtlcitUCo3rGcPjABA7tF0dClD3SIYuIiIiIiNTRbguGxxxzTKTDEBER2SdJ0XZOHpbCycNSKKkK8OWmYj7fUMzCbWUs2FbGfZ9CZg8Phw9M4LSo+EiHKyIiIiIiArTDgmEoFGLVqlW43W6eeuopgsEghx12GNdeey1xcXHN3p5hGBiGscey2t9qbx1BTZw1oe9bzLtfvmHw/+3deVyU1f4H8M+s7JuIYEqRAnKTbRAkAcudq7hcS81c0n6+1NLcrkuaN2/qzaVFu2qLZWoW7telXPOWaypogmY3E1QUQVllE2GYmfP7g3gSA2QIZsD5vF8vXrzmPIeZ7/M9zxzOfOeZeSBEQ+9//cVtKr8fLk3p+ABMkeuGOXaa3jFSEadcLocQ4vdWWdXtjYW55766HT9N8fgAfo/795idbVXo7++G/v5uKCzR4cS1fBxOuoPT1wvw480ivH8kFQEt7dHV2xnPtnXGY05W1d25xanI44P/z6kc80JERERE9a3RFQxzc3Px1FNPITo6GitWrMCdO3fw+uuvY+bMmfj000+Nvj9XV9dqt9nb2UOrN/yZcE3GTl3+Zfl2tvZQ1VPMdnb29XI/NT5GA8Td0GylmO2aTMyAaXNdn8dOUzxGbFQK6A0CLVq0qHJ7de2NhbnnPmOOn6Z4fACAWiEHALi7u1e53QOAj1drvNwVKCrV4fClTOy/eAuHL2XhQnoR/n3sJvxbOeKv7T3wV38PeLdwMGH0jVd1+SQiIiIiovrV6AqGzZs3R2xsrHTbxsYGM2fOxJAhQ1BUVAR7e+MKFTk5OSgrK6vUJpPJ4O7ujqK7RdDqmsgL0N++IP9ucRFKy/5czDJZ+Qv2u3eL0OAnQdVj3KYis6mI+S5Ky/RmjsYIJsh1gxw7TfAYgbUSCrkM/z7wE0ruO0ZksvLC1t1iEzy36sDBWoUJPdubLdd1On6a4vEBQK0sLxhmZGTU6mzTjh4K9AvqgOtptxCXko/DyXdw4mo+3vv2Mt779jKecLFGVx9ndPF2QTs3G4s7o6zi/3Zt82lpVCpVjW+QEhEREREZq9EVDC9dfBXTsAAAIElJREFUuoQ9e/Zg+vTp0gsirVYLuVwOtVpt9P0JIWp4cSF++2kKyuMs35U/F3PFRwHr475q8WgmfKz68fvh0pSOD8AUuW6YY6fpHSMVcZaU6aHV3V9UlkGlN/xW2Gp8+1KqM+Vz/4/qdvw0xeMD+D3umv4H/ZGVQobObZzQuY0TyvQGnLtZhCPJeTh6JQ/r429jffxteDio0cXbGV3aOiPgMTvILah4aGw+LQVzQkRERET1rdEVDJ2dnREbGwsnJye8/PLLyMzMxLvvvouBAwfWqWBIRETUFKkUcoQ/4YjwJxwxo6snLt66i8O/FQ83J2Ric0ImXG2VeKZtefEwpLUDlArLKR4SEREREVHDaXQFQw8PD6xevRrLli3Dxx9/DCsrK8TExGDmzJnmDo2IiMgsFHIZglrZI6iVPaY80wq/Zt7DkSt3cDg5Dzt/ysbOn7Jhr1agk5cjnmnrhKefcIK9lcLcYRMRERERURPV6AqGANCxY0ds3rzZ3GEQERE1OjKZDH7utvBzt8UrEa1wLeceDifn4cTVfBy6fAeHLt+BQg6EtHJA5zZOiGrjhJaOvOIyERERERHVXqMsGBIREVHtPOlqgyddbfB/4S2RWajFiWv5OHE1H2dvFuJMaiGWHb0Jn+Y2iGrjhGfaOKNdC8u7aAoRERERERmHBUMiIqJHRAsHNZ4LdMNzgW64q9XjzI0CHLuaj5PX8rEu/jbWxd+Gm70KUU+WX1glpLUDrH67ojMREREREVEFFgyJiIgeQXZqBbp4u6CLtwt0BoGLt+7i+NU8HL+aL33vobVSjg6e9ojwckInL0d+dJmIiIiIiACwYEhERPTIU8plCG5lj+BW9pjUuTVScktw4moeTqYU4HRKAX64VgAAeLKZNSKedESElxMCW9rzqstERERERBaKBUMiIiIL49XMGl7NPDAi1ANFpXrE3yj4rXiYj9gfMxH7Yybs1HJ0fNwRnbwc0cnLCc3tVOYOm4iIiIiITIQFQyIiIgtmb6VANx8XdPNxgUEIJGXdw8mUfJxMKcCR5DwcTs4DAPi62aCTlyPCn3CEv4cdVAp+9yERERER0aOKBUMiIqIGUturEVf0M/fVixUyGdq1sIWfux3+L/wx5N3TIe56AU6m5ON0Sj6+OJOBL85kwFYlR4inA8IfLy8gejpbNWjsD8uPEKLBHpuIiIiIyBKxYEhERFTPFHIZ9AYBd3d3o/7O2P4NQac3QPnb2YMeAPyeBEYB0BsEzt/Mw4mkbBxPysKplDycuJoPAGjlbINnfJujs48bItq6wtlW3SCxVZefMp0O2VlZDfKYRERERESWiAVDIiKieqaQy6CQy/DB/p9QqtPV4i9ksLezR9HdIgDmO1vOwVqFCT39Hxp3aCsbBLpbIS1fi5t5JUjNK8Wm+FRsik+FDICbvQqezlZo7WwFdwc1FPI/e/Zh9fmxUiowtXcgZDIZzzQkIiIiIqonLBgSERE1kFKdHlqdoRY9ZdDqDb/1NV/Rq1Snl37XJu5WTmq0clIj/AmgsESHtAIt0vJKkV6gRWZRGX68WQSlXAYPBxVaOqrR0tEKrnZKyI3++HLjyA8RERERkaVgwZCIiIj+NAdrJfyslfBrYQuDEMguKkNavhbpBeUFxJv5WgBFUCtk8HBQo6WjGo85qeFiozT7dzcSEREREVFlLBgSERFRvZLLZGjhoEYLBzU0sIdOL5BRpMWtgvKf1LxS3MgrBQBYKWXS2YePOarhZK1gAZGIiIiIyMxYMCQiIqIGpVTI0MrJCq2crAAAWr0BGYVluFVQilsFWqTkliIlt7yAaKOSw8NBDXcHFdwd1GhmW5ePMBMRERER0Z/BgiERERGZlFohh6ezFTydywuIpToDbhdocauw/AzEa7kluJZbAgBQKWRwt1fD01ULFysBN3sVlH/6IipERERERFQTFgyJiIjIrKyUcjzRzBpPNLMGUF5AzCwqQ0ahFrcLy78H8WZ++RmIchnQ3K787EN3BxVa/1Z0JCIiIiKi+sOCIRERETUqVsrKZyDqDMBdgxIpGfm4XaRFZmH5VZh/ulXeP7vkHP7R7TEzRkxERERE9GhhwZCIiIgaNaVchlaONnBS6hEEAYMQyLunQ2ZhGbLuluEvLR3NHSIRERER0SOFBUMiIiJqUuQyGZrZqtDMVoVApRwTu3rj9u3b5g6LiIiIiOiRITd3AERERERERERERNR4sGBIREREREREREREEhYMiYiIiIiIiIiISMKCIREREREREREREUkaZcEwJycHEyZMQGhoKMLDw/H2229Dp9OZOywiIiIiqgNj1nZHjx5Fv379EBwcjN69e+Pw4cMmjpaIiIiIGmXBcOrUqbC1tcXx48exfft2nDp1CuvXrzd3WERERERUB7Vd26WkpGDSpEmYMmUKzp49i0mTJmHq1KnIyMgwfdBEREREFqzRFQyvX7+O+Ph4zJw5EzY2NvD09MSECRMQGxtr7tCIiIiIyEjGrO127tyJ0NBQ9OjRA0qlEn369EFYWBi2bNlihsiJiIiILJfS3AE8KCkpCc7OznB3d5fa2rZti/T0dBQUFMDR0dGo+1Mq/7iLMpkMANC6mT3K9IY/F7CJ2KrL98PT1Q5a3Z+LWQbA2toGJVYCoh5iq0l9xm0qdr/F3LqZPbQ6vZmjqT1T5Lohjp2meIxUF7Mpn1t1Ye5c1yU/5o65royNu7EcO4013zXlR6Uof+9TpVJBiMb4zGt4Va11GhNj1nbJycnw9fWt9Pfe3t64dOlSnR+/seeHalaxbrfk5/ijgmP5aOA4Pjo4lo+OhlrrNLoV1N27d2FjY1OpreJ2cXGx0QVDZ2fnarcNj/QxOj5zGxbR9GIGmmbcwyK8zR1CnTTFXANNM+6mGDPQNONuijEDjNuUXF1dzR0CVcOYtV1Vfa2trVFcXFznx69pLUhNB5/jjw6O5aOB4/jo4FhSdRrdR5JtbW1x7969Sm0Vt+3s7MwREhERERHVkTFrOxsbG5SUlFRqKykp4RqQiIiIyMQaXcHQx8cHeXl5yM7OltquXLkCDw8PODg4mDEyIiIiIjKWMWs7X19fJCUlVWpLTk6Gj0/TO+uViIiIqClrdAVDLy8vdOjQAYsWLUJRURFSU1Px0UcfYdCgQeYOjYiIiIiMZMzarn///oiPj8e+ffug0+mwb98+xMfHY8CAAWaInIiIiMhyyUQj/HbL7OxsLFiwAHFxcZDL5fjb3/6GGTNmQKFQmDs0IiIiIjJSTWs7jUaD+fPno3///gCA48eP47333sONGzfQqlUrzJw5E88++6yZ94CIiIjIsjTKgiERERERERERERGZR6P7SDIRERERERERERGZDwuGREREREREREREJGHBkIiIiIiIiIiIiCQsGBIREREREREREZGEBUMiIiIiIiIiIiKSsGBIREREREREREREkiZfMMzLy8OsWbMQHh6OsLAwTJgwAZmZmQCA8+fPY/DgwdBoNOjWrRu2bdtm5mhN7+eff8bw4cMRGhqKqKgo/Otf/4JWqwVg2fnJzc1Fz549ERcXJ7U9LB87d+5Ez549ERwcjOeeew4JCQmmDtskqsrNwYMHMWDAAISEhKBbt25YtWoVDAaDtN1ScgNUnZ8KmZmZiIiIwI4dOyq1W3p+Ll26hFGjRkGj0SAiIgKLFy+GTqeTtltKfqrKzd69e9G7d2+EhIQgOjoamzZtqvQ3lpCbS5cu4eWXX0bHjh0RGRmJWbNmITc3FwDn5Zpyw3m5XE5ODiZMmIDQ0FCEh4fj7bffrjS/3O/o0aPo168fgoOD0bt3bxw+fNjE0VJ1jBnHTZs2ITo6GhqNBtHR0YiNjTVxtFQTY8aywuXLlxEUFFTl2orMw5hxjI+Pl/5XP/vss1i9erWJo6WaGDOWX3zxBbp164aQkBD069cPBw8eNHG09DA1vRatUK/rHdHEjRgxQkycOFHk5+eLwsJC8dprr4lx48aJvLw80bFjR/HVV1+JsrIycfLkSaHRaMT58+fNHbLJ6PV6ERkZKb744guh1+vFrVu3RHR0tFi1apVF5+fs2bOiR48ewtfXV5w+fVoIIR6aj9OnTwuNRiPOnj0rtFqtWLdunQgPDxfFxcXm3JV6V1VufvrpJxEYGCi+//57odfrRXJysujatav4/PPPhRCWkxshqs5PBb1eL0aOHCn8/PzEf/7zH6nd0vOTk5MjwsPDxSeffCK0Wq1ITU0VvXr1EmvWrBFCWE5+qsrNr7/+KoKCgkRCQoIQQogff/xRtG/fXpw5c0YIYRm5uXfvnoiMjBT//ve/RWlpqcjNzRVjx44V48ePt/h5uabccF7+3YgRI8T06dNFcXGxuHHjhoiJiRGfffbZH/pdu3ZNBAQEiEOHDomysjKxd+9eERgYKG7fvm2GqOlBtR3HQ4cOidDQUJGQkCAMBoM4d+6cCA0NFQcOHDBD1FSV2o5lheLiYtG3b98q11ZkPrUdx+TkZBEUFCR27NghDAaD+OWXX0THjh3F/v37zRA1VaW2Y3nkyBHRqVMnceXKFSGEEAcOHBB+fn4iNTXV1CFTNWp6LVqhvtc7TfoMw4sXL+L8+fNYsmQJHB0dYW9vj4ULF2LGjBn49ttv4ezsjOHDh0OpVKJTp07o16+fRb0LmZ+fj6ysLBgMBgghAAByuRw2NjYWm5+dO3dixowZmDZtWqX2h+Vj27ZtiImJQYcOHaBSqTB69Gi4uLhg37595tiNBlFdbtLS0jB06FB07doVcrkcbdu2Rc+ePXHmzBkAlpEboPr8VPjwww/h4eGBli1bVmq39Pzs2rULXl5eGD9+PFQqFVq3bo21a9eid+/eACwjP9XlJiUlBTqdTpqjZTIZFAoF1Go1AMvITXp6Ovz8/DBx4kSo1Wq4uLjghRdewJkzZyx+Xq4pN5yXy12/fh3x8fGYOXMmbGxs4OnpiQkTJlS5ltm5cydCQ0PRo0cPKJVK9OnTB2FhYdiyZYsZIqf7GTOOGRkZGDt2LIKDgyGTyaDRaBAeHi4d+2Rexoxlhfnz56NHjx4mjJIexphx3LhxI7p3746BAwdCJpPBz88PmzdvRocOHcwQOT3ImLG8evUqhBDSj0KhgEqlglKpNEPk9KCHvRa9v199rneadMHwwoUL8Pb2xtatW9GzZ09ERUVh6dKlcHNzQ1JSEnx9fSv19/b2xqVLl8wUrem5uLhg9OjRWLp0KQICAvDss8/Cy8sLo0ePttj8REVF4dChQ+jTp0+l9oflIzk5+ZHPV3W5iY6Oxpw5c6TbJSUlOHLkCNq3bw/AMnIDVJ8fADh9+jT27t2Lf/7zn3/YZun5uXDhAnx9fTFv3jxERkaiR48e+Prrr+Hh4QHAMvJTXW6ioqIQHByMF198Ee3bt8fQoUMxZcoUBAYGArCM3LRp0wZr1qyBQqGQ2g4ePIj27dtb/LxcU244L5dLSkqCs7Mz3N3dpba2bdsiPT0dBQUFlfpaSk6aImPGcfjw4Rg3bpx0OycnB2fOnIG/v7/J4qXqGTOWQPmbitevX8drr71myjDpIYwZxwsXLqB169b4+9//jvDwcPTu3Rvx8fFwc3MzddhUBWPGMiYmBs2bN0efPn3Qvn17TJkyBUuWLJHW7GReNb0WvV99r3eadMEwPz8fv/76K1JSUrBz507s2rULGRkZeP3113H37l3Y2NhU6m9tbY3i4mIzRWt6BoMB1tbWePPNN5GYmIg9e/bgypUrWLFihcXmx83Nrcp3SR6WD0vIV3W5uV9RUREmTpwIa2trjB49GoBl5AaoPj85OTl444038N5778HOzu4P2y09P/n5+dixYwcCAwNx5MgRrFq1Clu2bMG6desAWEZ+qsuNVqtF69atsW7dOpw/fx6rV6/GypUrceLECQCWkZv7CSGwfPlyHD58GHPnzuW8fJ8Hc3M/S56Xq9rPitsP7qul5KQpMmYc75eVlYWxY8fC398fffv2bdAYqXaMGcsrV65g+fLleP/99yu9MULmZ8w45ufnY8OGDejfvz9++OEHLFiwAEuXLsWBAwdMFi9Vz5ixLCsrg5+fH7Zt24bExEQsWLAAc+fOxa+//mqyeKl6tXmtDtT/eqdJFwwrPrY1d+5c2Nvbo3nz5pg6dSqOHj0KIQRKSkoq9S8pKanyBf2j6tChQzh48CCGDRsGtVoNHx8fTJw4EZs2bYKNjY3F5+d+D8sH81V+mvrQoUOh0+mwYcMG2NvbA7Ds3AghMGvWLIwcObLasxssOT9A+TwdEBCAQYMGQaVSwc/PDyNGjMD+/fsBWHZ+Vq5cCbVajYiICKhUKnTp0gUxMTHSRwYsKTdFRUWYPHkyvvnmG3z11Vdo164d5+XfVJWbCpY+L9va2uLevXuV2ipuP7ivlpKTpsiYcayQmJiIQYMG4cknn8THH3/Mj8w1ErUdy9LSUkybNg1vvPEGHnvsMZPGSA9nzHNSrVaje/fu6NKlC5RKJcLCwjBgwABpnUfmZcxYLly4ED4+PggMDIRarcbzzz+P4OBg7Ny502Tx0p9X3+udJl0w9Pb2hsFgQFlZmdRWcYXAv/zlL0hKSqrUPzk5GT4+PiaN0Zxu3bolXRG5glKphEqlgq+vr8Xn534Py4ePj49F5+vo0aMYPHgwOnfujM8//xxOTk7SNkvOza1btxAfH48PP/wQoaGhCA0NRXp6OubPn4/x48cDsOz8AOUfe3hwHrr/e1UtOT/p6emV/n8Bv8/RgOXk5saNG3j++edRVFSE7du3SwUxzsvV5wbgvAyU72deXh6ys7OltitXrsDDwwMODg6V+nLd03gZM44AsH37dowePRqjRo3C+++/L51AQOZX27H86aefkJKSgrlz50rrJwB45ZVX8NZbb5k6bHqAMc/JqtZ5er1eWueReRkzlunp6dXWDqjpqPf1Tp0uldJIaLVa0bNnTzFp0iRRVFQkcnJyxEsvvSQmTpwocnNzRWhoqFi3bp3QarXi1KlTQqPRiFOnTpk7bJNJSkoS/v7+4uOPPxY6nU7cuHFD9O3bVyxZsoT5EaLS1YUelo+Kq3OeOnVKuuJkWFiYuHPnjhn3oOHcn5uEhATRvn17sW3btir7WlpuhBA1Xpmqa9eula6SbOn5SU5OFv7+/uLTTz8VOp1OXLp0SXTu3Fl88cUXQgjLy8/9udm6dasIDAwUx44dEwaDQcTFxQmNRiO+//57IYRl5CYvL0906dJFzJ49W+j1+krbLH1erik3nJd/9+KLL4pp06aJwsJC6eqPK1as+EO/5ORkERAQIPbu3StdNTAgIEBcvXrVDFHTg2o7jgcOHBDt27cXx44dM0OUVBu1HcsH8SrJjUttx/HkyZPiqaeeErt27RIGg0HEx8eL4OBg8d///tcMUVNVajuWy5cvF+Hh4eLixYtCr9eL/fv3i4CAAPG///3PDFFTTWqaL+t7vdOkC4ZCCHH79m0xdepUERkZKUJDQ8WsWbNEfn6+EEKICxcuiBdeeEFoNBrRvXv3Si/iLcUPP/wgBg8eLDp06CC6dOkili1bJkpLS4UQzM+DT7SH5WPXrl0iOjpaBAcHi0GDBonExERTh2wy9+dm/Pjxol27diI4OLjSz5gxY6T+lpQbIYwrGArB/CQmJophw4aJ0NBQERUVJT788ENhMBik7ZaUnwdzs2HDBtGrVy+h0WhETEyM2L17d6X+j3pu1q5dK3x9fUVQUNAf5hghLHterik3nJd/l5WVJSZNmiQ6duwonn76abFkyRKh0+mEEEIEBwdXek4dO3ZM9O/fXwQHB4uYmBhx5MgRc4VND6jtOPbt21f4+fn94dh/8803zRk+3ceY5+T9WDBsXIwZxyNHjojnnntO+l+9adMmc4VNVajtWJaVlYkVK1aIrl27ipCQEDFw4EC+OdNIPThfNuR6RyYEzxcmIiIiIiIiIiKick36OwyJiIiIiIiIiIiofrFgSERERERERERERBIWDImIiIiIiIiIiEjCgiERERERERERERFJWDAkIiIiIiIiIiIiCQuGREREREREREREJGHBkIiogRUWFiI3N9fcYRARERERERHVCguGRGQ2sbGxaNeuHdavX2/uUIxy8+ZNtGvXDjdv3qxV/549eyIpKanOj9euXTvExcVVuW3kyJHw9/eHRqOBRqNBUFAQevfujU8//RQGg6FS36ysLCxYsADdunVDUFAQoqKiMH36dFy+fFnq89ZbbyEyMhI5OTmV/lan02HIkCEYP348hBB13hciIiIiIiJq/FgwJCKziY2NxYsvvogNGzZAp9OZO5wGc+fOnQa9//HjxyMhIQEJCQk4d+4c5s2bh9jYWMyfP1/qc/PmTQwcOBBZWVlYvXo1EhISsHv3bnh6emLIkCH44YcfAABz5sxB8+bNMWfOnEqPsXLlSmRnZ2Pp0qWQyWQNuj9ERERERERkXiwYEpFZnDp1Cjk5OZg9ezYMBgMOHjwobcvNzcWMGTMQFhaG8PBwTJs2Dfn5+QCA1NRUvPLKK+jQoQM6deqEt956C1qttsqz/lauXImRI0cCAHbs2IFhw4Zh6dKl6NixI55++ml8+eWX2Lp1K7p27YoOHTpg3rx50t9269YNO3bskG7HxcWhXbt2Ve7LuXPn8NJLLyEqKgoBAQF47rnnkJiYCACIjo4GAIwdOxafffYZAODkyZMYNGgQQkNDERMTg6+//lq6r7KyMixevBjh4eF4+umnsWbNGqPyqlAo0KlTJyxatAhbtmzB1atXAQCLFy+Gj48PVqxYAR8fH8jlcri6umLq1KkYOXIkZs+eDZ1OBysrKyxfvhxnzpzBl19+CQCIj4/H+vXr8cEHH8DZ2dmoeIiIiIiIiKjpYcGQiMziyy+/xJAhQ2BtbY1hw4Zh7dq10rYpU6agqKgI3377Lb777jsUFBRg/vz50Ol0GDNmDNzc3HDs2DHs2bMHiYmJWLlyZa0e88cff4S7uztOnz6NyZMnY/HixYiLi8O+ffuwfv16bN++HWfOnDFqP0pKSvDqq68iOjoax44dQ1xcHB5//HG88847ACAVQj/77DOMHTsWly5dwquvvopx48YhLi4OCxcuxKJFi3D8+HEAwEcffYQjR45g+/bt+P777yt9XNgYERERUKvViIuLg1arxdGjRzFgwIAqzw4cMmQIMjMzkZCQAABo06YN5s2bh/fffx+//PILZs+ejVmzZiEwMLBOsRAREREREVHTwoIhEZlcWloajh8/juHDhwMoL1glJycjPj4eaWlpiI+Px+uvvw4XFxfY29tjyZIlePXVV3Hu3DmkpaXhjTfegJ2dHVxdXbFq1SoMHjy4Vo9ra2uLUaNGQS6XIyoqCnq9HmPGjIGNjQ0CAgLQokULpKWlGbUvKpUKW7ZswbBhw6DVapGWlgZnZ2dkZGRU2X/z5s3o3r07evXqBYVCgZCQEAwZMgSxsbEAgN27d2PMmDHw9PSEra0t/vGPf9TpI8AymQxOTk7Iy8vDnTt3UFZWhubNm1fZt0WLFgCAzMxMqW3gwIHo1asXhg4dCo1GI40VERERERERPfqU5g6AiCzPxo0bodPpMGDAAKlNp9Nh7dq1eOWVVwAArVq1kra5ubnBzc0Ne/fuhYuLC2xsbKRtrVu3BoBaXYDE2dlZKr7J5eXvlzg6Okrb5XL5Hy4U8jAKhQJxcXEYO3YsiouL4e3tDaVSWe2FQdLS0nD69GmEhoZKbXq9Ho8//jiA8qJdy5YtpW2Ojo5wcnIyKiagPJ8FBQVwdXWFi4sLlEol0tPTq+xbkTs3N7dK7a+99hp2796NKVOmGP34RERERERE1HSxYEhEJlVaWort27fj7bffRkREhNR++fJljBs3DmPHjgUApKenw8vLCwCQnJyMPXv2oHPnzrhz5w7u3bsnFQ3Pnj2LixcvSt8VWFZWJt3ngxcbMeZMPblcXuN9VTh//jwWLlyIzZs3w9/fHwCwdu1aXLt2rcr+Hh4eGDhwIBYsWCC1ZWZmSgVGDw8PpKamStuKi4tRWFhY67grnDx5EqWlpejUqRPUajW6d++OHTt2YNCgQVKxtMK2bdvg5uYGjUZTqb2i34P9iYiIiIiI6NHGV4FEZFLffPMNZDIZ+vXrBw8PD+nnmWeega+vL3bt2oXIyEi88847KCgoQFFREd59912kpqYiMDAQXl5eWLp0Ke7du4fs7GwsXrwYubm5cHV1hZOTE/bu3QshBH7++WccOHCgznG2bdsW3333HUpKSpCVlYUNGzZU2a+wsBByuRzW1tYAgMTERGzYsAFarVbqo1arpaLfoEGDsGfPHpw4cQIGgwEpKSkYMWKE9B2OgwcPxpo1a3DlyhWUlpZiyZIl0Ov1tY5bp9Ph2LFjmDdvHkaNGgVPT08AwNy5c5Geno7JkyfjypUrMBgMyMjIwLJly7Bx40YsWrQIKpWqTrkiIiIiIiKiRwsLhkRkUhs3bkS/fv2qLE698MIL2L17N9555x3Y29ujd+/e6N69O5o1a4b58+dDpVLhk08+QUZGBrp06YIBAwYgLCwMkydPhlqtxsKFC7F//36EhIRgyZIlGDJkSJ3jnDFjBu7evYvIyEi89NJL6N+/f5X9IiMjMWzYMAwfPhxhYWGYP38+Ro4cidzcXGRnZ0v7NX36dCxfvhxBQUFYtmwZli1bhrCwMIwYMQLdunXD9OnTAZRfTbl///4YMWIEoqKi4ODg8NArE69evRoajQYajQYRERH44IMPMG7cOMyZM0fq4+7ujl27dqFly5YYN24cQkJC8Pzzz+PmzZvYunUrnnnmmTrnioiIiIiIiB4tMlHdF20RERERERERERGRxeEZhkRERERERERERCRhwZCIiIiIiIiIiIgkLBgSERERERERERGRhAVDIiIiIiIiIiIikrBgSERERERERERERBIWDImIiIiIiIiIiEjCgiERERERERERERFJWDAkIiIiIiIiIiIiCQuGREREREREREREJGHBkIiIiIiIiIiIiCQsGBIREREREREREZGEBUMiIiIiIiIiIiKS/D/atQJkd+iTLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1300x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
    "sns.set_context(\"paper\")\n",
    "\n",
    "SMALL_SIZE = 10\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=SMALL_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=SMALL_SIZE)  # fontsize of the figure title\n",
    "\n",
    "# create figure\n",
    "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(13,6))\n",
    "\n",
    "# 2016\n",
    "ax1 = sns.histplot(data= polder_den_2016, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax1)\n",
    "ax1.set_title('2016',fontweight =\"bold\")\n",
    "ax1.set_xlabel(\"Accumulated DOY\")\n",
    "ax1.set_ylabel(\"Number of Samples\")\n",
    "#ax1.set_ylim(0, 14)\n",
    "ax1.set_ylim(0, 35)\n",
    "ax1.set_xlim(60, 220)\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "# 2017\n",
    "ax2 = sns.histplot(data= polder_den_2017, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax2)\n",
    "ax2.set_title('Polder 22',fontweight =\"bold\")\n",
    "ax2.set_xlabel(\"Accumulated DOY\")\n",
    "ax2.set_ylabel(\"Number of Samples\")\n",
    "ax2.legend_ = None\n",
    "ax2.set_ylim(0, 35)\n",
    "ax2.set_xlim(60, 220)\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "# 2018\n",
    "ax3 = sns.histplot(data= polder_den_2018, x='Total_Lenght', kde = True, bins=11, hue='obs',ax=ax3)\n",
    "ax3.set_title('2018',fontweight =\"bold\")\n",
    "ax3.set_xlabel(\"Accumulated DOY\")\n",
    "ax3.set_ylabel(\"Number of Samples\")\n",
    "ax3.legend_ = None\n",
    "ax3.set_ylim(0, 35)\n",
    "ax3.set_xlim(60, 220)\n",
    "#ax1.set_xlim(data_all_rice.doy_SOS.min(), data_all_rice.doy_SOS.max(),10)\n",
    "\n",
    "\n",
    "fig.tight_layout(pad=1)\n",
    "\n",
    "plt.rc('legend', loc=\"upper right\")\n",
    "plt.savefig(\"D:\\crop_season_stats\\data\\2016_2018.svg\", format='svg')\n",
    "\n",
    "#Total_Lenght"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  },
  "toc-autonumbering": true,
  "toc-showmarkdowntxt": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
